Flood susceptibility mapping using convolutional neural network frameworks

Journal Pre-proofs Research papers Flood susceptibility mapping using convolutional neural network frameworks Yi Wang, Zhice Fang, Haoyuan Hong, Ling Peng PII: DOI: Reference:

S0022-1694(19)31217-X https://doi.org/10.1016/j.jhydrol.2019.124482 HYDROL 124482

To appear in:

Journal of Hydrology

Received Date: Revised Date: Accepted Date:

24 September 2019 2 December 2019 16 December 2019

Please cite this article as: Wang, Y., Fang, Z., Hong, H., Peng, L., Flood susceptibility mapping using convolutional neural network frameworks, Journal of Hydrology (2019), doi: https://doi.org/10.1016/j.jhydrol.2019.124482

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© 2019 Published by Elsevier B.V.

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Flood susceptibility mapping using convolutional neural

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network frameworks

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Yi Wang1,*, Zhice Fang1, Haoyuan Hong2,3,4,5*, Ling Peng6

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430074, China

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Universitätsstraße 7, 1010 Vienna, Austria

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Ministry of Education, Nanjing, 210023, China

Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan Department of Geography and Regional Research, University of Vienna, Key Laboratory of Virtual Geographic Environment (Nanjing Normal University),

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(Jiangsu Province), Nanjing, 210023, China

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Development and Application, Nanjing, Jiangsu 210023, China

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6 China

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*Correspondence

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([email protected])

State Key Laboratory Cultivation Base of Geographical Environment Evolution Jiangsu Center for Collaborative Innovation in Geographic Information Resource Institute of Geo-Environment Monitoring, Beijing 100081, China Author: Yi Wang ([email protected]); Haoyuan Hong

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1. Introduction

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Floods are one of the most common and catastrophic geo-hazards that are poorly

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comprehended (Tehrany et al., 2014a). The severe impact of floods on natural

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ecosystems and human activities has greatly affected economic and social

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sustainability (Rahmati et al., 2015). Therefore, it is essential to identify flood-prone

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zones to prevent or mitigate adverse effects of flooding (Bathrellos et al., 2017; Zhu et

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al., 2015).

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In the past few years, various statistical and machine learning (ML) methods have

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been successfully applied in flood susceptibility mapping (FSM) (Bui et al., 2019b;

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Mojaddadi et al., 2017). Statistical methods are mainly based on the assumption that

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historical flood events are closely related to flood predisposing factors, including

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frequency ratio (Rahmati et al., 2016; Tehrany et al., 2015a), logistic regression

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(Youssef et al., 2015a), weights of evidence (Tehrany et al., 2014b; Youssef et al.,

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2015b), analytic hierarchy process (Kazakis et al., 2015) and multiple criteria decision

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(Liu et al., 2018; Santos et al., 2019a; Santos et al., 2019b; Wang et al., 2019b).

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Recently, ML techniques have been applied to FSM, such as artificial neural network

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(Bui et al., 2019a; Kia et al., 2012), decision tree (Choubin et al., 2019; Khosravi et

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al., 2018; Tehrany et al., 2013), Random Forest (Rizeei et al., 2018) and Support

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Vector Machine (SVM) (Choubin et al., 2019; Tehrany et al., 2014b; Tehrany et al.,

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2015a; Tehrany et al., 2015b).

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In general, when applying an ML framework to solve a particular problem, feature

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engineering is a critical step that obtains an appropriate feature representation from

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the raw data (e.g., pixel values of the image) prior to data modeling. Furthermore, the

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performance of ML algorithms depends to a large extent on the representation of the 2

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raw data (Goodfellow et al., 2016). In this sense, ML methods cannot directly uncover

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instructive representations from raw data, nor can they obtain new insights from these

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representations, thus further improving predictive capability (LeCun et al., 2015).

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More recently, deep learning, as one of the most popular ML techniques, has received

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widespread attention and has been able to obtain reliable results comparable to or

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superior to conventional ML methods (Schmidhuber, 2015). This technique includes a

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broad class of methods for computer vision, signal processing and natural language

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processing with multiple network architectures (Graves et al., 2013; Hinton et al.,

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2012; Sutskever et al., 2014). Among various deep learning methods, a convolutional

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neural network (CNN) with architecture inspired by biological visual perception can

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identify representation with extreme variability through convolutional and pooling

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layers (Wang et al., 2019a). Gebrehiwot et al. have successfully applied CNN to

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identify flood ranges using unmanned aerial vehicle data (Gebrehiwot et al., 2019).

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Moreover, several feature engineering methods of correlation-based, information gain

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ratio and multi-collinearity analysis have been used in flood susceptibility analysis

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(Bui et al., 2019b; Khosravi et al., 2019; Zhao et al., 2019). Therefore, since CNNs

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can get rid of the tedious feature engineering steps and extract useful information

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directly from the original data, they can play an important role in FSM. Although

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CNNs are more accurate than the state-of-the-art ML methods in many fields

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(Ghorbanzadeh et al., 2019; Salamon and Bello, 2017; Simard et al., 2003; Yu et al.,

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2017), few studies have applied this technique to FSM.

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Choosing mapping units is a key step for flood susceptibility modelling. When

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applying the ML method to FSM, grid cells (pixels) are commonly used mapping

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units that have the advantage of being easier to process (Bui et al., 2019a; Das, 2019a;

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Termeh et al., 2018). Essentially, FSM is a binary classification task that determines 3

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whether a pixel will be a flood point and predicts its probability of occurrence. In the

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FSM process, each grid cell is considered as a feature vector that contains information

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about different triggering factors, which can be processed by the ML models.

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Therefore, the entire study area can be regarded as a multi-channel “image”, where

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each channel represents a flood triggering factor layer, which makes it possible to

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apply the CNN technique to flood susceptibility analysis. In this paper, the CNN

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framework is introduced to assess flood susceptibility in Shangyou County, China.

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CNNs can use different convolutional operations to extract various information from

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different data modalities. The three main contributions of this paper are outlined as

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follows. First, to the best of our knowledge, the application of CNN in FSM is still

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very rare. We explore the feasibility of CNNs in FSM in this study. Second, two CNN

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frameworks for FSM are presented. Specifically, for the first framework, the CNN

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model is used directly as a classifier to assess flood susceptibility. For the second

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framework, CNN is integrated with the SVM classifier in a hybrid manner: (1) the

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CNN model is used to extract powerful and useful representation of flood triggering

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factors, and (2) the SVM classifier is selected for classification with the extracted

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features. Third, three data presentation methods are designed in the CNN architecture

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to adapt to the classification and feature extraction in the FSM process. To validate

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and compare the predictive capability of the proposed CNN-based methods to SVM,

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several objective criteria of overall accuracy (OA), kappa coefficient (), receiver

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operating characteristic (ROC) and area under ROC curve (AUC) were used.

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2. Study area and available data

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2.1. Study area

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Shangyou County is located in the southern part of Jiangxi Province, China, and is

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in the northern hilly district of Nanling. The study site covers an area of

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approximately 1543 km2 between 25°42′N to 26°01′N and 114°00′E to 114°40′E. The

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Shangyou district has a total population of approximately 3.22 million people and the

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elevation ranges between 110 and 1901 m above sea level (Fig. 1). Shangyou County

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is located in the southern subtropical zone and belongs to the humid monsoon climate

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zone in the subtropical hilly region. In this study area, the climate is mild, rainfall is

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abundant, sunshine is sufficient, four seasons are distinct, and the frost-free period is

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long. Specifically, the average annual rainfall during 1959–2014 ranged between

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933.7 and 2147.6 mm, according to the Shangyou Meteorological Bureau. The

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rainfall displays a great difference in spring and summer and the annual rainy season

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in Shangyou district is from April to August. The average annual sunshine time is

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1708.3 h, which is an intermediate level in Jiangxi Province, and the average annual

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temperature is 18.6 ℃. Moreover, January and July are the coldest and hottest month

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with an average temperature of -2.7 ℃ and 38 ℃, respectively. Vegetation coverage

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accounts for more than 95% of the study area, mainly including farmland, forests and

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grasslands. Meanwhile, the study area is rich in water resources. The annual average

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runoff of surface water in Shangyou County is 3.52 billion cubic meters. The

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groundwater is mainly loose pore water, bedrock fissure water and underground hot

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water. In short, from a climate perspective, the study area is rich in precipitation and

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extreme weather events. As a result, the area is vulnerable to climatic factors that

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cause flooding. 5

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Fig. 1.

Location of the study area.

2.2. Flood inventory mapping

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A flood inventory map records the locations of the inundated area and provides

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detailed information about the characteristics of historical flood events (Santos et al.,

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2019a; Zazo et al., 2018). In this study, a flood inventory map with 108 historical

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flood events was provided by the Jiangxi Meteorological Bureau1 and the Department

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of Civil Affairs of Jiangxi province2, as shown in Fig. 1. The locations of flood events

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were obtained through historical records, extensive field surveys and visual

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interpretation of Google Earth images. All the sampling points in Fig. 1 represent

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where the flood occurred and were used to produce training and validation sets: 76

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locations (70%) were randomly selected for training, whereas 32 locations (30%)

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were used for validation. Moreover, the same number of non-flood locations (76 and 1 2

http://www.weather.org.cn http://www.jxmzw.gov.cn 6

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32) were randomly selected from the area free of flood to construct the training and

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validation sets.

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2.3. Flood triggering factors

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According to literature review (Arabameri et al., 2019; Khosravi et al., 2019;

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Shafizadeh-Moghadam et al., 2018) and characteristics of the study area, 13 flood

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triggering factors were selected for FSM, including altitude, aspect, curvature, slope,

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stream power index (SPI), sediment transport index (STI) and topographic wetness

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index (TWI), lithology, land use, normalized difference vegetation index (NDVI),

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soil, distance to rivers and rainfall.

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Altitude is a frequently used flood triggering factor and lower altitudes are usually

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accompanied by higher river discharge (Bout and Jetten, 2018; Sowmya et al., 2015;

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Wang et al., 2016). Aspect is the orientation or direction of the maximum slope of the

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terrain surface and influences hydrologic conditions because different orientations

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face different effects of precipitation and solar radiation (Bathrellos et al., 2018; Xu et

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al., 2016). Curvature describes the degree of distortion of the slope surface and

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reflects the morphology of the topography in a given area (Mojaddadi et al., 2017).

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The curvature layer can be computed from digital elevation model (DEM) data using

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fourth order surface model (Zevenbergen and Thorne, 1987). Slope directly affects

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surface runoff and vertical percolation since water flows from higher to lower altitude

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(Chaabani et al., 2018). SPI represents the performance of river sediment transport

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and fluvial channel erosion (Pamučar et al., 2017) and is calculated as follows (Moore

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and Wilson, 1992): SPI  As tan 

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(1)

where As is the area of the basin and  is the slope gradient (in degrees). STI 7

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qualitatively explains the process of erosion and deposition (Tehrany et al., 2015a)

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and is calculated as follows (Moore et al., 1993): 0.6

1.3

 A   sin   STI   s     22.13   0.0896 

(2)

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where the parameters in Eq. (2) are defined the same as SPI. TWI is a physical

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attribute that reflects the situation of geotechnical wetness (Chapi et al., 2017), and it

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is computed as follows (BEVEN and Kirkby, 1979):

   TWI  ln    tan  

(3)

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where  and  represent the upslope area per unit contour length and the slope

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angle, respectively. Lithology determines the geological engineering characteristics of

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the study area (Arabameri et al., 2019; Hong et al., 2018b), and the lithological

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classes of the study area are described in Table 1. Land use contributes to flood

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occurrence because it may alter flood runoff and sediment transport (Das, 2019b;

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Rizeei et al., 2018). NDVI can accurately display surface vegetation coverage (Liang

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et al., 2017) and is calculated as follows:

NDVI 

RNIR  RR RNIR  RR

(4)

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where RNIR and RR represent the spectral reflectance acquired in near-infrared

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region and in red region, respectively. Different soil surface characteristics have

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diverse potential of water storage, which significantly affect the water balance (Hong

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et al., 2018a). The distance to rivers indicates the distance from river networks, which

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are the main pathways for flood discharge and expansion (Gigovi´C et al., 2017;

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González-Arqueros et al., 2018; Qazi et al., 2016). Continuous rainfall events

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recharge basins and aquifers, and thus the average annual rainfall was always 8

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considered as a flood triggering factor (Arnell and Gosling, 2016; Tehrany et al.,

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2015b).

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The related information on all the triggering factors is listed in Table 2. It should

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be noted that the land use factor was obtained by classifying a Landsat 7 ETM+

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satellite image (Scene ID: LE71220422001324SGS00) obtained on November 20,

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2001 by using the maximum likelihood algorithm with an OA above 85%, and the

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river networks were extracted from the topographic map and the distance to rivers

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factor was calculated by using Euclidean tool. All the factor layers were converted

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into a raster format with a grid size of 30  30 m that corresponds to the spatial size of

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the DEM data. All the continuous factors were reclassified into categorical classes

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using ArcGIS software, based on the previous studies (Chen et al., 2019; Gigović et

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al., 2017; González-Arqueros et al., 2018; Mojaddadi et al., 2017), the characteristics

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of flood spatial distributions and the numerical range of these factors. The

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reclassification maps of all the flood triggering factors are shown in Fig. 2.

(a)

(b)

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(c)

(d)

(e)

(f)

(g)

(h)

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(i)

(j)

(k)

(l)

(m) 187 188 189

Fig. 2. Thematic maps of the study area. (a) Altitude, (b) aspect, (c) Curvature, (d) distance to rivers, (e) land use, (f) lithology, (g) NDVI, (h) rainfall, (i) slope, (j) soil, (k) SPI, (l) STI and (m) TWI.

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Table 1 Lithological types of the study area. Group name

Unit name

Lithology

A

Yang Jiayuan group; Zishan group

Dolomite, calcareous Carbonaceous siltstone

B

Zhongpeng group; Mashan group

Sandstone, silty shale, siltstone

C

Huangxie unit; Changshan unit;

D

Tangbiangroup; Hekou group

unit;

Xia

Xia Huangkeng group; Chang kengshui group; Dui Ershi group Nan Pingshan unit; Tanghu super unit; Fufang super unit Yingqian super unit; Huang Tiankeng unit

E F G

Monzonitic granite, potassium granite Conglomerate, mudstone, Pebbly Sandstone Sericite slate, siliceous slate Tonalite, phyric granodiorite, porphyry monzogranite, Granodiorite

H

Bali group; Lao Hutang group

Phyllite, sandy slate, feldspathic quartz sandstone

I

Shuishi group, Gaotan group

Silty slate, carbonaceous slate

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196

Haihui

siltstone,

Table 2 Data sources and the associated factors in the study area. Flood trigger factors

Source of data

Scare/Resolution

Altitude Aspect Curvature Slope SPI STI TWI

ASTER GDEM Version 2

30 m

Lithology

China Geology Organization

1:2,000,000

Land use NDVI

Landsat 7 ETM + images

30 m

Soil

Institute of Soil Science, Chinese Academy of Sciences

1:1,000,000

Distance to rivers

ASTER GDEM Version 2

30 m

Rainfall

Jiangxi Meteorological Bureau

1:50,000

3. Methodology

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The flowchart of the proposed methodology to identify flood susceptibility zones is

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illustrated in Fig. 3. Specifically, a flood inventory map and flood triggering factors

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are first prepared to construct training and validation sets. Then, three data 12

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representation forms are used to train different dimensional CNN architectures. Next,

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the trained CNN architectures are applied for classification and feature extraction in

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the FSM process. Finally, the prediction results obtained by different CNN-based

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methods are quantitatively evaluated using several objective criteria.

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Fig. 3.

Flowchart of the proposed methodology.

3.1. SVM

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SVM can map original data into high dimensional feature space by using kernel

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functions (Cortes and Vapnik, 1995) and attempt to find an optimal hyperplane that

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separates true and false classes (Vapnik, 1999). Recently, it has been widely used in

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flood susceptibility assessment (Choubin et al., 2019; Tehrany et al., 2019; Tehrany et

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al., 2014b; Tehrany et al., 2015b). 13

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B N Assuming that a training set x   x1 , x2 , . . . , xN   consists of two classes that

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denoted as L  1,  1 , the aim of SVM is to ensure hyperplane margin

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maximization and error minimization. The error minimization is expressed as follows: 1 min  w w , i , b 2 

2

  C  i  i 

(1)

s.t. y i  w    xi   b   1   i ,  i  0 ,  i  1, 2, . . . , l 215

where w is the weight vector, b denotes the bias,  i is the slack variables for

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non-separable data and C denotes the penalty parameter. The optimization (1) is

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solved as follows:  l  1 l max    i    i j yi y j K  x , xi   i 2 i 1, j 1   i 1

(2)

l

s.t. 0   i  C ,   i yi  0 i 1

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where  i is Lagrange multipliers and K  x, xi  is the kernel function, the decision

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function is written as follows: l

f  x     i yi K  x, xi   b

(3)

i 1

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The SVM with the radial basis function (RBF) kernel can achieve excellent

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performance in classification/prediction tasks due to the following facts: First, the

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RBF kernel function can map a sample to a higher dimensional space, and the linear

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kernel function is essentially a special case of RBF. Meanwhile, the kernels of RBF

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and sigmoid have similar performance with certain parameters. Second, compared

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with the polynomial kernel function, only a few parameters need to be modulated by

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RBF and the number of kernel function parameters directly influences the complexity

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of this function. Finally, the RBF kernel has less numerical difficulties. 14

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3.2. Data representations for CNNs

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As a powerful technique, CNN structures of different dimensions can be

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constructed to adapt to input data of different dimensions, which can greatly affect

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final classification/prediction results (Kussul et al., 2017). In addition, the

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representation of raw data is crucial because CNN is based on the principle of locality

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(Li et al., 2019). In this section, three data representations will be introduced to fit the

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novel CNN architectures, as illustrated in Fig. 4. Initially, 13 flood triggering factor

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layers are stacked together to build a multi-channel “image” with a spatial resolution

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of 30 m, which is regarded as input data for further data transformation. To clarify,

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the three data representations are briefly described as follows:

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(1) For the one-dimensional data form in Fig. 4 (a), each pixel vector is considered

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as an image with several flood triggering attributes hidden in each pixel.

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Specifically, the input data consists of a set of column vectors whose length is

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determined by the number of flood triggering factors. Each element in the

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vector represents the corresponding flood predisposing factor’s attribute value.

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(2) As shown on the right side of Fig. 4 (a), the two-dimensional data form is

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derived from one-dimensional data. The most critical issue is how to construct a

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bridge from a one-dimensional vector to a two-dimensional matrix. In this

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study, each pixel vector in the multi-channel “image” is converted into an n  n

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matrix in a specific way, where n is the maximum between the number of flood

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triggering factors and the number of categories in which each factor is

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reclassified. For example, there are 13 flood triggering factors in the study area,

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which are larger than the maximum categories (9) of the aspect and lithology

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factors. Thus, a value of 13 is assigned to n. Then, each value in the pixel vector 15

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(one-dimensional data) is expanded into a row vector of length 13. Specifically,

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the fourth element of “6” (the attribute value of the distance to rivers factor) in

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the pixel vector is converted into a row vector of

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which the sixth and other elements of the vector are assigned values of 1 and 0,

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respectively. Finally, all row vectors form a 13 × 13 matrix (two-dimensional

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data).

0 0 0 0 0 1 0 0 0 0 0 0 0 , in

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(3) In the case of three-dimensional data, each pixel and its adjacent pixels form a

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three-dimensional window patch, as shown in Fig. 4 (b). The patch has the

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same label as the current pixel. Assuming the window size is 5, each data

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sample is 13 × 5 × 5.

(a)

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(b) 262 263

Fig. 4.

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3.3. CNN architectures

Different data forms. (a) One-dimensional and two-dimensional data forms, (b)

three-dimensional data form.

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CNNs, exhibiting robust performance in computer vision and image processing

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fields, are basically multilayer feed-forward neural networks that can automatically

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extract valuable features from raw data (Zhang et al., 2018). The CNN architecture is

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mainly composed of an input layer, multiple hidden layers and an output layer, and

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the hidden layers consist of one or more convolutional and pooling layers

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(Ghorbanzadeh et al., 2019; LeCun et al., 2015). The convolutional layer that consists

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of several convolution kernels iteratively extracts sophisticated and effective features

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from the original data (Canziani et al., 2016; Mallat, 2016). The pooling (sub-sample)

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layer is usually carried out after a convolutional layer to reduce the dimensionality of

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feature maps through a down-sampling algorithm, which can avoid overfitting and

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reduce computational cost (Chen et al., 2016). Moreover, CNNs have strong adaptive

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capability and can achieve excellent performance when facing different dimensional 17

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data (Shin et al., 2016). In this section, three different dimensional CNN architectures

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are developed for regional flood susceptibility analysis.

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3.3.1. 1D-CNN

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The 1D-CNN structure is comprised of five layers of an input layer, a convolutional

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layer, a max pooling layer, a fully connected layer and an output layer, which was

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developed and trained using one-dimensional data. Assuming that there are n flood

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triggering factors in the input data, N convolutional kernels with a size of a  1 to

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filter the input data, the resultant layer has N feature vectors. Each grid cell is

285

connected to an a  1 neighborhood in the input vector. A b  1 max pooling layer is

286

immediately used after the first convolutional layer, which can reduce the length of

287

feature vectors. A fully connected layer with k neural units follows the max pooling

288

layer to reorganize the extracted representations. Finally, m neural units are viewed as

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the output of the network. Fig. 5 shows the architecture of 1D-CNN.

290 291

Fig. 5.

1D-CNN architecture ( n = 13, N = 20, a = 3, b = 2, k = 20 and m = 2 ). 18

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3.3.2. 2D-CNN

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The two-dimensional CNN structure consists of two convolutional layers, two max

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pooling layers and one fully connected layer. Assuming that the input data is an n  n

295

image and the first convolutional layer has N kernels with a size of a  a, which can

296

produce N feature maps, the size of feature maps is reduced by half with the depth

297

remaining unchanged after applying a b  b max pooling operation. The resulting

298

feature maps are sent to the second convolutional layer that has M kernels with a size

299

of a  a. Then, the resultant M feature maps are sent to the second b  b max pooling

300

layer, and a fully connected layer with k neural units are used to recognize these

301

extracted features. Finally, m neural units are regarded as the output of the 2D-CNN

302

architecture. Fig. 6 shows the structure of 2D-CNN.

19

303 304

Fig. 6.

2D-CNN architecture ( n = 13, a = 3, N = 20, M = 10, b = 2, k = 50 and m = 2 ) .

20

305

3.3.3. 3D-CNN

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The 3D-CNN structure contains one convolutional layer, one max pooling layer

307

and two fully connected layers. Assuming that each input data can be represented as a

308

c  n  n matrix, N convolutional kernels with a size of a  a  a to filter the input

309

data, the resultant layer has N feature maps. The b  b  b max pooling layer is used

310

to reduce the size of feature maps by half. Then, two fully connected layers with k

311

neural units are used to reorganize the extracted information. Finally, the output layer

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containing m neural units, representing the final result of the whole network. Fig. 7

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shows the structure of 3D-CNN.

314 315 316

Fig. 7.

3D-CNN architecture ( c = 13, n = 5, N = 20, a = 3, b = 2, k = 45 and m = 2 ).

3.3.4. Hyperparameters of CNN

317

Hyperparameters setting is a key step in building a CNN architecture (LeCun et al.,

318

2015). This section illustrates some related hyperparameters used in this study. 21

319

Specifically, the sizes of the convolutional kernel and the pooling kernel determine

320

the scale of the convolution and pooling operations, respectively (Choi et al., 2017;

321

Golik et al., 2015; Simard et al., 2003). The activation function converts a linear

322

relationship to a nonlinear relationship so that the neural network can approximate

323

any nonlinear function (Audebert et al., 2019; Chen et al., 2016; Dahl et al., 2013;

324

Mou et al., 2017). The loss function is used to measure the degree of inconsistency

325

between the predicted value and the true value (Chen et al., 2014; James and Stein,

326

1992; Nourani et al., 2015; Ranjbar et al., 2018; Singh, 1997). In addition, the

327

optimizer is used to iteratively update parameters at different learning rates through a

328

gradient descent algorithm (Hinton et al., 2012). AdaGrad is a widely used optimizer

329

because it can automatically adjust the learning rate based on the gradient value of the

330

independent variable in each dimension, thereby avoiding the problem of difficulty in

331

adapting to the unified learning rate in all dimensions (Duchi et al., 2011; Paoletti et

332

al., 2018; Schmidhuber, 2015). The learning rate is critical to the training process

333

because it controls the learning speed of the CNN architecture (Marmanis et al., 2016;

334

Salamon and Bello, 2017).

335

3.4. FSM using CNNs

336

CNNs are characterized with local connections and sharing weights, and their main

337

advantage is to effectively extract spatial information and automatically optimized

338

network parameters (Krizhevsky et al., 2012; LeCun et al., 1998). To explore the

339

predictive capability of the CNN framework, two different perspectives of

340

classification and feature extraction are proposed in flood susceptibility analysis.

341

Specifically, in the first perspective, CNNs are directly used as classifiers for FSM,

342

whereas in the second perspective, a hybrid framework is developed by integrating 22

343

CNNs with the SVM classifier, and the CNNs are used to extract more representative

344

features from original data, improving classical ML classifiers to achieve more

345

effective prediction with these representative features.

346

3.4.1. CNN classifiers

347

As mentioned in Section 3.2 and 3.3, CNNs have achieved fairly good results in

348

processing classification related tasks (Hu et al., 2015). In the field of flood

349

susceptibility assessment, the main objective is to predict where a flood event will

350

occur, which can be treated as a binary classification process to label a given region

351

with “flood” or “non-flood”. In this study, the CNNs are directly used as classifiers

352

for regional flood spatial prediction. The main steps of applying CNNs for

353

classification are summarized as follows. First, the original data are reformed

354

according to the data representation algorithms previously mentioned in Section 3.2.

355

Then, a CNN structure is built based on the dimensionality of the reformed data. After

356

a series of convolutional and pooling operations, the high-level feature maps are

357

extracted from the input data and then reorganized by a fully connected layer. Finally,

358

the reorganized feature vectors are converted to resultant classification results using a

359

nonlinear activation function. For simplicity, the generalized classification process

360

using a CNN classifier with the 2D-CNN architecture is shown in Fig. 8.

23

361 362 363

Fig. 8.

Generalized CNN classifier with the 2D-CNN architecture.

3.4.2. Integration of CNNs and SVM

364

CNN is an effective ML technique that can discover new representations and

365

relationships from the original data, and these features are not easily uncovered

366

through traditional methods (Bergen et al., 2019). Furthermore, CNNs can

367

automatically extract image features including color, shape, texture, and topological

368

structures by using different combinations of neurons and learning rules, which

369

proved the great potential in feature extraction as well (Niu and Suen, 2012). In other

370

fields, the integration of CNNs and SVM has achieved promising results. For

371

example, Niu and Suen (Niu and Suen, 2012) used a 2D-CNN with five layers to

372

extract features from Mixed National Institute of Standards and Technology data and

373

applied SVM for final classification. Also, the hybrid method of CNN and SVM was

374

applied for microvascular morphological type recognition and hyperspectral image

375

classification (Leng et al., 2016; Xue et al., 2016). In this study, different CNN

376

structures are used to learn more suitable features from the original data. Based on

377

this idea, the main steps of integrating CNN and SVM are summarized as follows. 24

378

First, the original data is fed to the CNN structure in different dimensional styles.

379

Then, after several layers of convolution and pooling manipulations, the hidden

380

information of the input data is reorganized in the fully connected layer, where the

381

neural units are regarded as the extracted features. In the following, the SVM

382

classifier is trained using the extracted high-level features. Finally, this integrated

383

framework is used to perform FSM. Specifically, three different dimensional CNN

384

feature extractors are proposed to learn high-level features from the original data with

385

the same structures mentioned in Section 3.3. Fig. 9 illustrates the classification

386

process using the 2D-CNN architecture to extract features for an example.

387 388 389

Fig. 9.

Integrated classifier using a ML classifier, couple with CNNs for feature extraction.

390

3.5. Evaluation methods

391

3.5.1. Feature evaluation

392

To evaluate the effectiveness of extracted features by CNNs, a similarity measure

393

(mean value of correlation coefficients) among all the samples is used. The greater the

394

similarity of extracted features in the same class indicates that the input data is easier

395

to discriminate. For clarification, the correlation coefficient is defined as follows (Goh 25

396

et al., 2007):

  x1 , x2  

C  x1 , x2  D  x1  D  x2 

(1)

397

where x1 and x2 represent two feature vectors, C  x1 , x2  is a covariance matrix,

398

and D  x1  and D  x2  are the variance of x1 and x2 , respectively.

399

3.5.2. Model evaluation

400

Recently, the ROC curve has been used to assess the performance of FSM models

401

(Dano et al., 2019; Kazakis et al., 2015; Popovic et al., 2018). Specifically, this curve

402

is constructed using “sensitivity” on the y-axis against “1-Specificity” on the x-axis

403

(Bradley, 1997). Moreover, an AUC value indicates the predictive capability as well

404

(Choubin et al., 2019; Pamučar et al., 2018b; Xia et al., 2017). In particular, an AUC

405

value of 1 indicates that there is a perfect spatial agreement between the observed and

406

simulation data. An AUC value of 0.5 is the agreement that would be expected due to

407

chance, whereas a value of 0 considers a non-informative result (Choubin et al., 2019;

408

Fawcett, 2006; Tehrany et al., 2014b). In addition, OA is another widely used

409

criterion to assess the predictive performance of FSM models (Chapi et al., 2017;

410

Hong et al., 2018b; Rahmati et al., 2016), and it is the proportion of the number of

411

correctly classified pixels to the total number of pixels as follows: OA 

TP  TN TP  TN  FP  FN

(2)

412

where TP (true positive) and TN (true negative) denote the number of flood and

413

non-flood grid cells that are correctly classified, where FP (false positive) and FN

414

(false negative) are the number of flood and non-flood grid cells incorrectly classifier,

415

respectively. Besides,  is another powerful statistical criterion measuring the 26

416

agreement or reliability between two raters (Pontius Jr and Millones, 2011). For flood

417

susceptibility assessment, this criterion can evaluate the prediction accuracy of FSM

418

models and a higher value implies better prediction (Khosravi et al., 2018; Mahmoud

419

and Gan, 2018). This criterion is formulated as follows:

κ

Pexp 

PC  Pexp 1  Pexp

((TP  FN )(TP  FP)  ( FP  TN )( FN +TN )) (TP  TN  FN  FP)

(3) (4)

420

where PC is the proportion of samples that have been correctly classified and Pexp

421

means the expected probability of change agreement.

422

Additionally, a non-parametric test of Wilcoxon signed-rank test was implemented

423

to validate the statistical significance difference among different models (Wilcoxon et

424

al., 1970). The null hypothesis is based on the pre-assumption that FSM models have

425

no significance difference at a significance level of  = 5%. Generally, if the p value

426

is smaller than 0.05 and the Z value beyond the threshold of 1.96, the prior

427

hypothesis is not true and rejected and the difference between two models is

428

significant.

429

4. Results

430

4.1. Building flood models and producing flood susceptibility

431

maps

432

In this subsection, three different CNN architectures were constructed for

433

classification and feature extraction in flood susceptibility analysis. To this end, three

434

different dimensional training sets and validation sets were first prepared for 27

435

subsequent CNN training and validation, as described in Section 3.3. Then, three

436

different dimensional CNN structures were constructed, and training sets of different

437

dimensions were fed to the corresponding neural network with a certain number of

438

iterations until the training process converges. Finally, trained CNN models were used

439

for classification and feature extraction. It should be noted that the window size is a

440

key parameter for constructing a three-dimensional data form. Therefore, as shown in

441

Fig. 10, analysis of window size is performed. It can be observed that a 3D-CNN

442

model with a window size of 5 performs the highest AUC value. In addition, during

443

the construction of the CNN structure, the related hyperparameters were determined

444

through a fine-tuning step using the validation set, referring to previous studies

445

(Audebert et al., 2019; Hu et al., 2014; Maggiori et al., 2017; Shin et al., 2016). Table

446

3 reports all hyperparameters settings for different CNN architectures. The

447

aforementioned CNN structures were built in Python using the Keras framework3.

448 449

Fig. 10. Analysis of window size for three-dimensional data.

450 3

https://keras.io 28

451 452 453

Table 3 Hyperparameter settings of the CNN architectures. Parameters

CNNs

Convolutional kernel size

Max pooling kernel size

Number of epochs

1D-CNN

31

21

600

2D-CNN

33

22

400

3D-CNN

333

222

150

Activation function

Loss function

Optimizer

Learning rate

ReLU

Categorical cross-entropy

AdaGrad

0.0015

454

As the proposed CNN architectures were constructed and trained, three CNN

455

structures were applied to classification and feature extraction. Regarding CNN for

456

classification, 1D-CNN, 2D-CNN and 3D-CNN models were used to calculate the

457

flood susceptibility index for each pixel. For CNN for feature extraction, the validity

458

of the extracted features was evaluated using the similarity statistical index, as shown

459

in Table 4. It can be observed that all the extracted features obtained by the 1D-CNN,

460

2D-CNN and 3D-CNN architectures have higher similarity than the original features,

461

which demonstrates that the extracted features are more efficient and easier to

462

classify. Then, the learned features were regarded as new representation vectors and

463

sent to the SVM model for classification, and the susceptibility indexes were

464

calculated by the hybrid methods of 1D-CNN-SVM, 2D-CNN-SVM and

465

3D-CNN-SVM. After the susceptibility index of each grid cell in the study area was

466

obtained by the FSM models, these indices were automatically divided into five

467

intervals of very low, low, moderate, high and very high using the natural breaks

468

(Jenks) method (Jenks, 1967), which has been widely used in the FSM process (Chapi

469

et al., 2017; Dano et al., 2019; Shafizadeh-Moghadam et al., 2018). The SVM

470

classifier with RBF kernel is a robust method that has achieved good performance in

471

FSM (Choubin et al., 2019; Tehrany et al., 2015b; Zhao et al., 2019). For comparison,

472

an SVM classifier with an RBF kernel was used for FSM to demonstrate the 29

473

effectiveness of a CNN-based feature extractor.

474 475

Table 4 Similarity of original features and extracted features. Quantitative Methods Original 1D features Extracted features Original 2D features Extracted features Original 3D features Extracted features

Similarity Flood class Non-flood class 0.723 0.508 0.934 0.894 0.487 0.275 0.933 0.839 0.705 0.494 0.911 0.772

476

The flood susceptibility maps produced by different CNN-based methods and SVM

477

are shown in Fig. 11. It can be seen that very high susceptible zones have similar

478

distributions and most floods are located in very high susceptible areas. In order to

479

quantitatively analyze the susceptibility maps, the frequency of flood occurrence in

480

the susceptible zones was analyzed, and the results are shown in Table 5 . It can be

481

seen that most floods are predicted in the high and very high susceptible areas and

482

very few floods occur in very low susceptible areas, which proves that there is

483

relatively high consistency between flood historical events and susceptible zones for

484

all the methods. Furthermore, more than 80% of the flood historical events located in

485

the very high susceptible area confirmed the rationality of the flood susceptibility

486

maps. Meanwhile, all the CNN-based methods achieved more reliable susceptibility

487

maps because the frequency ratio of floods was higher than SVM in the very high and

488

high areas.

30

(a)

(b)

(c)

(d)

(e)

(f)

31

(g) 489 490

Fig. 11. Flood susceptibility maps of different classifiers. (a) 1D-CNN; (b) 2D-CNN; (c) 3D-CNN; (d) 1D-CNN-SVM; (e) 2D-CNN-SVM; (f) 3D-CNN-SVM and (g) SVM.

491 492 493 494 495 496

Table 5 Frequency analysis of floods on the susceptibility maps.

Susceptible area Very low Low Moderate High Very high 497

1D-CNN 0% 0% 2.78% 15.74% 81.48%

2D-CNN 0.93% 0% 2.78% 12.96% 83.33%

3D-CNN 0% 0.93% 1.85% 12.04% 85.19%

1D-CNN-SVM 0% 0.93% 4.63% 11.11% 83.33%

2D-CNN-SVM 0% 0.93% 6.48% 8.33% 84.26%

3D-CNN-SVM 0.93% 1.85% 1.85% 11.11% 84.26%

4.2. Model validation and comparison

498

To evaluate the performance of all the methods, the statistical criteria of  and OA

499

were used, as shown in Table 6. It can be seen that all the CNN-based methods

500

achieved better performance than SVM in terms of both OA and . For example, the

501

1D-CNN-SVM, 2D-CNN-SVM and 3D-CNN-SVM methods were 1.57–4.69% and

502

0.0312–0.0937 higher than SVM in terms of OA and , respectively. Moreover, the

503

2D-CNN-SVM method achieved the highest OA and  value among all the methods.

504

Therefore, the performance of SVM can be further improved by using the features

505

extracted by CNN. 32

SVM 0% 4.63% 5.56% 6.48% 83.33%

506

Table 6 Prediction accuracies of different CNN-based methods and SVM. Method 1D-CNN 2D-CNN 3D-CNN 1D-CNN-SVM 2D-CNN-SVM 3D-CNN-SVM SVM

OA value 84.38% 85.94% 84.38% 85.94% 87.50% 84.38% 82.81%

 0.6875 0.7188 0.6875 0.7188 0.7500 0.6875 0.6563

507

Fig. 12 shows the ROC curves of all the methods using the validation set.

508

Experimental results demonstrated that all the CNN-based methods had better

509

prediction performance than SVM in terms of AUC. Specifically, the highest AUC

510

value obtained by the 2D-CNN method was 0.937, followed by 2D-CNN-SVM

511

(0.934), 3D-CNN (0.928), 3D-CNN-SVM (0.922), 1D-CNN (0.905), 1D-CNN-SVM

512

(0.904) and SVM (0.883). Meanwhile, when CNN was used for feature extraction in

513

the classification process, the AUC value of SVM can be increased by 0.021–0.051.

514

Furthermore, the Wilcoxon signed-rank test was selected to verify the statistical

515

difference between the proposed CNN-based methods and the SVM classifier. When

516

the significance level of p value is smaller than 0.05 and the Z value exceeds 1.96,

517

the difference between flood models is significant. Z values and the significant levels

518

of different flood models are shown in Table 7. It can be seen that all the comparative

519

models are very different, because the Z-value and p-value of the significance level

520

satisfy with the corresponding significant conditions mentioned in Section 3.5.2.

33

521 522

Fig. 12. ROC curves for all the methods using the verification set.

523

Table 7 Wilcoxon signed-rank test between the proposed methods and SVM. Comparative pairs SVM vs. 1D-CNN SVM vs. 2D-CNN SVM vs. 3D-CNN SVM vs. 1D-CNN-SVM SVM vs. 2D-CNN-SVM SVM vs. 3D-CNN-SVM

524

Z value -8.575 -2.925 -11.145 -14.657 -14.469 -11.145

p value < 0.05 < 0.05 < 0.05 < 0.05 < 0.05 < 0.05

Significance level Yes Yes Yes Yes Yes Yes

5. Discussion

525

It is a key prerequisite for preventing and reducing flood damage by conducting an

526

appropriate FSM technique (Hong et al., 2018a; Wang et al., 2019b). Therefore, it is

527

necessary to explore the possibility of applying newly developed techniques in FSM.

528

In this paper, the deep learning representative of CNN is considered for regional flood

529

susceptibility analysis with a different perspective in Shangyou County, China.

530

As a popular and powerful technique, CNN gradually shows great potential in

531

classification/prediction. CNN architectures of different dimensions have been

532

designed to solve various tasks with different data modalities (LeCun et al., 2015). 34

533

For example, 1D-CNN has been widely used for signal processing and sentence

534

classification, 2D-CNN has been mainly used for image processing (Golik et al.,

535

2015; Hu et al., 2014) and 3D-CNN has been developed for video related problems (Ji

536

et al., 2012; Tran et al., 2015). With regard to flood susceptibility analysis, the sample

537

in the study area is usually composed of a set of factor vectors in the form of

538

one-dimensional data (Chapi et al., 2017; Hong et al., 2018a; Khosravi et al., 2019;

539

Zhao et al., 2019), and all the samples are input into the ML method for classification.

540

To fully explore the prediction capability of CNN in flood susceptibility assessment,

541

data forms of different dimensions were proposed to fit the corresponding CNN

542

structure. The one-dimensional data form contains all the information about the flood

543

triggering factors, and 1D-CNN architecture can take advantage of the local

544

relationships between the flood triggering factors. Various flood triggering factors

545

related to the morphological, geological and hydrological conditions play a crucial

546

role in constructing flood models (Mahmoud and Gan, 2018b; Rijal et al., 2018).

547

Based on the fact that CNN has demonstrated the promising results in image

548

processing (Anthimopoulos et al., 2016; Krizhevsky et al., 2012; LeCun et al., 1998),

549

a two-dimensional data form is proposed to construct the CNN to analyze flood

550

susceptibility in an “image” perspective. This means that this data form can be

551

regarded as a two-dimensional extension of the feature vector by considering the

552

spatial information of the triggering factors. As for the three-dimensional data form, it

553

contains both the attribute factor information and local terrain spatial information

554

between pixels in a specific window.

555

It is significant for decision-makers to produce reliable flood susceptibility maps to

556

assess and prevent flood hazards (Termeh et al., 2018; Wang et al., 2019c; Xia et al.,

557

2017). Meanwhile, it is a useful way to assess flood susceptibility maps using 35

558

frequency analysis of flood occurrences. In Table 5, most floods occurred in the high

559

and very high susceptible areas and few floods occurred in the very low susceptible

560

areas, which is consistent with previous studies (Bui et al., 2019b; Chapi et al., 2017;

561

Khosravi et al., 2018). Moreover, the CNN-based methods achieved a higher flood

562

frequency than SVM (89.81%), reaching 92.59–97.22%, in terms of the high and very

563

high susceptible areas, which proved that the CNN-based methods can achieve more

564

accurate and reliable susceptibility maps. Furthermore, experimental results in Table

565

6 and Fig. 12 demonstrated that all the proposed CNNs obtained better results than

566

SVM in terms of OA,  and AUC. SVM learns the decision plane directly from the

567

raw data, while CNN can transform the raw data into useful representations that are

568

important for model discrimination. After convolution and pooling operations,

569

important parts of input data are enhanced, and irrelevant information is reduced.

570

Furthermore, the Wilcoxon signed-rank test in Table 7 demonstrated that the

571

statistical difference between the CNN-based methods and SVM is significant. It was

572

verified that the improvement in prediction accuracy brought by the CNN-based

573

methods is instructive for decision-makers considering adopting a novel CNN

574

technique to obtain more accurate flood susceptibility maps. In particular, when the

575

2D-CNN architecture was used directly as a classifier or feature extraction in the FSM

576

process, the reasons for obtaining the highest accuracy can be summarized as follows.

577

First, as described in Section 3, compared to the one-dimensional data form, the

578

three-dimensional data form contains not only attribute information but also local

579

terrain spatial information. However, the three-dimensional data form may contain

580

redundant information that interferes with the classifier to distinguish true

581

classification labels. Second, the two-dimensional data form not only makes the

582

construction of the CNN network easier, but also can better display the attribute 36

583

information in the original data.

584

It should be noted in Table 6 that some methods had the same OA and  values.

585

For example, 1D-CNN, 3D-CNN and 3D-CNN-SVM obtained the same OA and 

586

values of 84.38% and 0.6875, and 2D-CNN and 1D-CNN-SVM got the same OA and

587

 values of 85.94% and 0.7188, respectively, due to the very limited validation

588

samples. In addition, the evaluation criteria of OA and  that mainly consider the final

589

classification label may not accurately reflect the significant differences between the

590

proposed FSM models, but the ROC curve technique is very sensitive to flood

591

susceptibility indices. Therefore, the ROC curve and AUC value can precisely portray

592

the reliable assessment results of the FSM methods, which has been confirmed by

593

other studies (Liu et al., 2018; Mukhametzyanov and Pamucar, 2018; Pamučar et al.,

594

2018a; Zhao et al., 2019).

595

6. Conclusions

596

This paper studies the application of the CNN technique in FSM from two different

597

perspectives in the case of Shangyou County, China. First, a spatial database

598

containing 13 flood triggering factors and 108 flood historical events was prepared to

599

construct the proposed methods. The developed CNN architectures were then used to

600

construct flood susceptibility models in two different ways of classification and

601

feature extraction. Next, flood susceptibility maps were obtained using the

602

CNN-based methods in comparison to SVM. Finally, several objective criteria of OA,

603

, ROC and AUC were used to compare and verify all the FSM methods. The main

604

conclusions of this paper are summarized as follows:

605

(1) The proposed 1D-CNN, 2D-CNN and 3D-CNN classifiers achieved better 37

606

prediction performance than SVM in terms of AUC, which proves the superiority of

607

the CNN frameworks.

608

(2) By using CNN for feature extraction, the prediction capability of SVM was

609

effectively improved. In particular, the 2D-CNN-SVM method achieved the highest

610

accuracy in terms of OA,  and AUC.

611

(3) The proposed CNN-based methods are expected to be used for flood disaster

612

assessment and management. Meanwhile, the application prospect of CNN can inspire

613

more effective FSM techniques.

614

Acknowledgements

615

This work was supported by the National Natural Science Foundation of China

616

(61271408, 41602362), the International Partnership Program of Chinese Academy of

617

Sciences

618

(201906860029). The authors would also like to thank the handling editor and the two

619

anonymous reviewers for their valuable comments and suggestions, which

620

significantly improved the quality of this paper.

621

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44

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Abstract

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Flood is a very destructive natural disaster in the world, which seriously threaten the

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safety of human life and property. In this paper, the most popular convolutional neural

896

network (CNN) is introduced to assess flood susceptibility in Shangyou County,

897

China. The main contributions of this study are summarized as follows. First, the

898

CNN technique is used for flood susceptibility mapping (FSM) through two different

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CNN classification and feature extraction frameworks. Second, three data

900

presentation methods are designed in the CNN architecture to fit the two proposed

901

frameworks. To construct the proposed CNN-based methods, 13 flood triggering

902

factors related to flood historical events in the study area were prepared. The

903

performance of these CNN-based methods was evaluated using several objective

904

criteria in comparison to the classic support vector machine (SVM) classifier.

905

Experiments results demonstrate that all the CNN-based methods can help produce

906

more reliable and practical flood susceptibility maps. For example, the proposed

907

CNN-based classifiers were 0.022–0.054 higher than SVM in terms of area under the

908

curve (AUC). In addition, in the classification process, CNN-based feature extraction

909

can effectively improve the prediction capability of SVM by 0.021–0.051 in terms of

910

AUC. Therefore, the proposed CNN frameworks can help mitigate and manage

911

floods.

912

Keywords: Flood susceptibility mapping; convolution neural network; classification;

913

feature extraction; China.

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45

Credit Author Statement

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Yi Wang: Conceptualization; Formal analysis; Funding acquisition;

919

Supervision; Roles/Writing - original draft

920

Zhice Fang: Data curation, Methodology, Validation; Visualization

921

Haoyuan Hong: Investigation; Resources; Writing - review & editing;

922

Funding acquisition;

923

Ling Peng: Funding acquisition; Writing - review & editing

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Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

933 934 935 936 937

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Highlights

939



CNNs are considered for dealing with the flood susceptibility mapping task. 46

940



Two different CNN frameworks of classification and feature extraction are presented.

941

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

Three data presentation forms are designed for the proposed CNN frameworks.

943



Reliable flood susceptibility maps can be obtained by using the proposed CNNs.

944



Prediction performance of SVM can be improved using the CNNs for feature

945

extraction.

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47