Suitability evaluation of urban construction land based on geo-environmental factors of Hangzhou, China

Computers & Geosciences 37 (2011) 992–1002

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Suitability evaluation of urban construction land based on geo-environmental factors of Hangzhou, China Kai Xu a, Chunfang Kong a,b, Jiangfeng Li c, Liqin Zhang c, Chonglong Wu a,d,n a

School of Computer, China University of Geosciences, Wuhan 430074, China Key Lab of Biogeology and Environmental Geology of Ministry of Education, China University of Geosciences, Wuhan 430074, China c Faculty of Earth Resources, China University of Geosciences, Wuhan 430074, China d Three Gorges Research Center for Geo-hazard, Ministry of Education, China University of Geosciences, Wuhan 430074, China b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 12 January 2010 Received in revised form 28 January 2011 Accepted 4 March 2011 Available online 12 April 2011

Suitability evaluation of urban construction land based on geo-environmental factors is the process of determining the fitness of a given tract of land for construction. This process involves a consideration of the geomorphology, geology, engineering geology, geological hazards, and other geological factors and is the basis of urban construction land planning and management. With the support of Geographic Information Systems (GIS), grid analysis, and geo-spatial analysis techniques, four factor groups comprising nine separate subfactors of geo-environmental attributes were selected to be used in the evaluation of the suitability level for construction land in Hangzhou. This was based on K-means clustering and back-propagation (BP) neural network methods due to their advantages in fast computing, unique adaptive capacity, and self-organization. Simultaneously, the evaluation results based on K-means clustering and BP neural network were compared and analyzed, and the accuracy evaluation was set. The results showed that the geo-environmental suitability evaluation results of construction land based on K-means clustering and BP neural network were similar in terms of the distribution and scale of construction land suitability level. At the same time, the results of the two evaluation methods were consistent with the variability in suitability level, engineering geology, and hydrogeology of Hangzhou. The results also showed that the real advantage of the methods proposed in this paper lies in their capacity to streamline the mapping process and to ensure that the results are consistent throughout. The suitability level of the urban construction land based on the geoenvironment in Hangzhou was divided into four construction sites: land for building super high-rise and high-rise buildings, land for building multistorey buildings, land for low-rise buildings, and nonbuilding land. The results of the suitability evaluation for each category will provide a scientific basis for decision-making in urban development in Hangzhou. Crown Copyright & 2011 Published by Elsevier Ltd. All rights reserved.

Keywords: Suitability evaluation Grid K-means clustering Back-propagation (BP) neural network Geographic Information System (GIS) Analytic Hierarchy Process (AHP) Hangzhou China

1. Introduction In 2008, for the first time ever, more than half of the planet’s population resided in urban areas (United Nations Population Fund (UNPF), 2007). Urbanization has profoundly transformed natural landscapes throughout the world (Theobald et al., 2000; Luck and Wu, 2002; Chiesura, 2004; Colding, 2007; Tzoulas et al., 2007; Beardsley et al., 2009). Urban areas have the most concentrated human activities and the most intensified land use. This land use change causes drastic disturbances to the environment, and the environment presents responses to the engineering activities of human beings in turn (Li, 2000). Meanwhile, inappropriate use of

n Corresponding author at: School of Computer, China University of Geosciences, Wuhan 430074, China. Tel.: þ86 27 67883286; fax: þ 86 27 67883051. E-mail address: [email protected] (C. Wu).

the geo-environment and improper development of geological resources are becoming increasingly significant, directly restricting construction and development of the city. For example, excessive pumping of groundwater has led to lowering of water levels and significant land subsidence in many urban areas (the largest land settlement reached 2.63 m from 1921 to 1965 in Shanghai, and 2.04 m from 1959 to 1981 in Tianjin, respectively) and the Karst collapse (Nanning City) (Wu, 1994). Therefore, reducing geoenvironmental deterioration of urban land resources has become a challenge in urban construction. This is why it is critical to consider geo-environmental factors in evaluating the suitability level of urban construction land. To date, most researchers have focused on urban environmental engineering and geological quality assessment by using multivariate statistical analysis and GIS (Rybar, 1973; Anonymous, 1976; Matula, 1981; Varnes, 1984; Du, 1989; Price et al., 1996; Griffiths, 2002; Cross, 2002; Lee et al., 2004; Lu et al., 2005; Sarkar

0098-3004/$ - see front matter Crown Copyright & 2011 Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.cageo.2011.03.006

K. Xu et al. / Computers & Geosciences 37 (2011) 992–1002

et al., 2007; Turer et al., 2008; Price, 2009). Others have studied urban geological disaster risk evaluation by using a comprehensive analysis method (Mejı´a-Navarro and Garcı´a 1996; Cook et al., 1998; Jia and Fang, 1999; Yenigul, 2000) and urban land suitability analysis by using fuzzy classification methods and multicriteria analysis (Hall et al., 1992; Pereira and Duckstein, 1993; Davidson et al., 1994; Van Ranst et al., 1996; Store and Kangas, 2001; Wang et al., 2005), as well as urban ecological suitability assessment for urban development and planning by using ecology methods (Lathrop and Bognar, 1998; Svoray et al., 2005; Chen et al., 2006; Liang et al., 2007; Yang et al., 2009). In recent years, the suitability evaluation of urban construction land in China has been investigated using different methods in different locations, including geological classification of foundations and their suitability for high-rise buildings in urban areas of Guangzhou (Lin and Ma, 1996), geo-environmental evaluation for urban land-use planning in Lanzhou City based on GIS (Dai et al., 2001), construction land suitability evaluation of Gaochun District in Nanjing (Wang et al., 2005) and Nanchong City (Yang, 1997) with the assistance of the GIS method, construction land geo-environmental quality of Nanchang City by using fuzzy clustering comprehensive evaluation (Zhang et al., 2007), and Heihe City with a composite index evaluation model (Lou et al., 2007). All these studies are of great significance in the development of suitability evaluation for urban construction land. Most of these study methods are knowledge driven. However, subjectivity is an issue with these techniques. There is thus a need to develop expert systems that keep the knowledge-driven advantages while reducing human errors in data processing. Therefore, K-means clustering and the BP neural network have been used for their advantages in fast computing, unique adaptive capacity, and self-organization in evaluating and forecasting purposes in various fields, including in urban environmental quality evaluation (Lek and Guegan, 2000; Bai et al., 2001). The objective of this paper is, therefore, to evaluate the geoenvironmental suitability level of urban construction land based on geo-environmental factors and the land use status by using K-means clustering and BP neural network techniques, as well as to consider the urban development characteristics and expansion direction of Hangzhou. The evaluation results may provide important scientific information to improve decision-making for urban construction land planning, management, and use of Hangzhou.

993

2. Methods 2.1. Study area Hangzhou, the capital of Zhejiang province, is the well-known historical, political, economic, cultural, scientific, and educational center of Zhejiang. It lies to the west of Hangzhou Bay and near the lower reaches of the Qiantangjiang River and the southern end of the Jinghang Canal, so it is an important transport hub in southeast China. The study area is located at 1191400 –1201440 east longitude and 291500 –301340 north latitude and covers about 3068 km2 urban area of Hangzhou, including eight districts, which are Shangcheng, Xiacheng, Gongshu, Jianggan, West Lake, Binjiang, Xiaoshan, and Yuhang (Fig. 1). The west, middle, and south study areas belong to the middle-low mountainous and hilly regions of West Zhejiang, where the Karst land and banded valley flats are quite common. In contrast, the northwest study area belongs to the northern plain of Zhejiang, where the land is low and flat, is covered with a dense river network, and is part of Hangjiahu Plain and Ningshao Plain, a typical ‘‘southern rivertown.’’ The complicated terrain and landforms of the study area have been impacted by drastic paleoclimate changes, multiphase tectonic movements, as well as alluviation and deposition in the recent past of the Tiaoxi River and the Qiantangjiang River. As a result, the study area has many sedimentary deposit types, various and complicated facies changes, vertically alternating soft and hard soil layers, and large variations in thickness of complex Quaternary stratigraphic sequences.

2.2. General approach Seven procedures were used in evaluating the suitability of Hangzhou for urban construction land based on geo-environmental factors. First, the suitability evaluation factor index system for construction land was confirmed based on geo-environmental factors. Second, the suitability evaluation units for construction land were divided into 250  250 m2 raster grid cells by using MapGIS software. Each cell was considered as a homogeneous unit for any given factor. Third, 11 thematic maps of Hangzhou were collected as data sources in this study included: geological

Fig. 1. Map of the study area of Hangzhou, Zhejiang Province, China.

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bedrock at 1:100,000, landforms and Quaternary at 1:100,000, tectonic outline at 1:200,000, depth-to-bedrock contour at 1:200,000, hydrologic geology with 1:200,000, geological map of the Karst subregion at 1:25,000, stability sectional map of Karst around the West Lake at 1:25,000, engineering geology at 1:100,000, foundation load at 1:300,000, typography interpretation map of remote sensing image, and urban environmental geochemistry survey map. Fourth, spatial and attribute databases of different geo-environmental factors were established by the vector-based approach using MapGIS software. Fifth, all the vector maps that reflect different geo-environmental factors were standardized and overlaid in the analysis with the 250  250 m2 grid cells to obtain a score for each subfactor in each grid unit, respectively. Sixth, the weights of each factor and subfactor were obtained by different experts and the Analytic Hierarchy Process (AHP) method (Banai-Kashani, 1989; Saaty and Vaargas, 1991; Carver 1991; Eastman et al., 1995; Bandyopadhyay et al., 2009), and then an integrated value of evaluation unit was calculated (see Eqs. (1)–(3)). Finally, the evaluation results for construction land suitability were obtained by K-means clustering and BP neural network methods, as well as by the transforming of MapGIS. Our overall strategy of analysis is summarized in the flowchart shown in Fig. 2.

2.3. Establish evaluation factors system Urban construction land suitability level is a result of the interactions of each evaluation factor, so proper selection of evaluation factors is critical to ensure meaningful suitability evaluation results for construction land (Chen and Xu, 1997; Joerin et al., 2001). Integrating the urban construction land characteristics of Hangzhou and the special requirements for suitability evaluation, four factor groups comprising nine separate and sensitive geo-environmental subfactors were selected from the above-noted maps for a suitability evaluation of Hangzhou urban construction land using AHP: geomorphic type, slope, site soil type, Holocene saturated soft soil depth, stratum steadiness, groundwater salinization, groundwater abundance, geological hazard type, and geological hazard degree. They form a two-level hierarchical structure according to their subordinate relationship (Fig. 3). In the geo-environmental suitability evaluation process of construction land, a primary step is to ensure a standardized measurement system according to the degree of importance of all factors in the evaluation (Dai et al., 2001). Second, it is important to analyze the relationship between the geological environmental factors and the urban land use and development. Table 1 shows the compatibility between the geological environmental factors

Confirmation of suitability evaluation geo-environmental factors index system for construction land

Division the evaluation unit using MapGIS software

Collection and collation of data geological map tectonic outline map bedrock depth contour map landform and Quaternary geological map hydrologic geological map geological map of Karst sub-region stability sectional map of Karst around West Lake engineering geology map foundation load map typography interpretation map of remote sensing multi-media urban environmental geochemistry survey map

Establishment of spatial attribute databases Vectorize maps Establish attribute database Rasterize vector maps

Standardization of sub-factors Ensure evaluation factor classified standard Assigned to each sub-factors

Computation of weights of sub-factors Calculate weights for each sub-factors Calculate integrated values for each sub-factors

Multi-criteria evaluation K-means clustering Back-Propagation (BP) neural network Evaluation results analysis Fig. 2. Flowchart of suitability evaluation for construction land based on geo-environmental factors.

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Geo-environmental construction land suitability evaluation factors

Geomorphology

Hydrological geology

Engineering geology

Geomorphic type Slope Site soil type

Stratum steadiness

Holocene saturated soft soil depth

Geologic hazard

Groundwater abundance Geologic hazard degree

Groundwater salinization

Geologic hazard type

Fig. 3. Geo-environmental construction land suitability evaluation factor system.

and the construction land in Hangzhou. Table 1 shows that the closer the compatibility, the better the conditions for urban development in Hangzhou. Each subfactor was scored using a paired comparison according to its influence on construction land geo-environmental suitability. In this study, construction land suitability was first classified into five levels: levels I, II, III, IV, and V. These five levels were assigned scores of 9, 7, 5, 3, and 1, respectively. A positive correlation between the value awarded and the suitability was employed. In other words: the greater the score, the higher the level for development of urban construction land (Zhang et al., 2009). Table 2 shows the class boundaries and standardized measurements employed for each factor. It should be noted that various statistical and empirical guidelines from the related national codes and the literature were used in determining the boundary values for the urban construction land evaluation. This study organized four geological factors and nine subfactors into an ordered two-level hierarchical structure with AHP. The relative importance of each factor and subfactor was identified using a paired comparison. Their weight was decided according to their relative importance ranking with AHP. In addition, we designed a weight survey table for 100 experts who were familiar with the geological environment of Hangzhou, and we asked them to assign a weight for each the geo-environmental factor according to their impact on the construction land in Hangzhou. That is the higher the weight of the factor, the greater the impact of its importance for urban construction land; conversely, the lower the weight of the factor, the lower the impact. Also, these principles are true to the subfactors of his parent factors. In other words, the weights of the subfactors are based on their impact on the importance of the parent factors, and their total weights are 100, too. We obtained the weight of each factor and subfactor by weighing the average of all the experts’ survey tables. Therefore, the final weight of each factor and subfactor also took into account the score given by the experts and AHP (Table 3), which provided the needed data for the suitability evaluation of the Hangzhou construction land based on the geo-environmental factors. 2.4. Divide evaluation units An evaluation unit is the basic spatial unit for the geoenvironmental suitability evaluation of urban construction land (Kalogirou, 2002). The evaluation unit division aims to objectively reflect the spatial difference in construction land geo-environmental quality. According to the uniformity and difference principle of land quality, the study area was divided into grids of 250  250 m2 as an evaluation unit with MapGIS software before the geo-environmental suitability evaluation of the urban construction land in Hangzhou. The evaluation unit division helps to perform spatial overlay and model calculation of attribute data, while reducing the subjectivity of the evaluation process.

Moreover, each grid is an information retrieval source as well as a unit for showing the evaluation results. The study area was divided into 56,332 raster grid cells in Hangzhou. All the maps listed above were, respectively, overlaid with the analysis by a 250  250 m2 grid to obtain a score for each subfactor in each grid unit. Based on each subfactor’s score, an attribute database of the subfactor system for the suitability evaluation of Hangzhou construction land was established. 2.5. Assign the score for the evaluation subfactors The geo-environmental suitability level of urban construction land tries to summarize the complex interaction among many factors and subfactors. If the influence of each factor and subfactor is assigned a certain score and weight, the accumulated results will reflect the comprehensive influence of the construction land based on a grid. It is assumed that m factors have been selected for the evaluation, and that each factor includes n subfactors. Then each factor’s score in a construction land evaluation unit equals the weighted sum of each subfactor’s score. That is Pi ¼

n X

Fj Wj ,

ð1Þ

j¼1

where Pi refers to the score of factor i, Fj refers to the score of subfactor j of factor i, and Wj refers to the weight value of subfactor j. If P is the total score of a construction land evaluation unit and Wi is the weight value of factor i, the total score of P is P¼

m X

Pi Wi :

ð2Þ

i¼1

Taking Pi into Eq. (2), we get P¼

m X n X

Fj Wj Wi :

ð3Þ

i¼1j¼1

Four factors were selected: topography, engineering geology, hydrogeology, and geological hazard; so m¼4. The four factors were subdivided into nine subfactors: geomorphic type, slope, soil type, stratum steadiness, Holocene saturated soft soil depth, groundwater abundance, groundwater salinization, geological hazard type, and geological hazard degree; so n ¼9. 2.6. Based on K-means clustering suitability evaluation The calculation was performed with K-means clustering of Statistical Product and Service Solutions (SPSS) based on the above established model. In fact, K-means clustering (MacQueen, 1967) is a method commonly used to automatically partition a data set into k groups (Hartigan and Wong, 1979; Bradley and Fayyad, 1998;

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L L M M L L M M L L M F F F F F F F F F F N N N N M N N N L N N N

L L L L L L L L L L L

M M M M M M M M M M M

(1) Each instance d is assigned to its closest cluster center. (2) Each cluster center K is updated to be the mean of its constituent instances.

F F F F F F F F L F F

N N N N N N N N N N F

L L L L L L L L L L F

F F F F F F F F F F F

N N N N N N N N N N N

The algorithm converges when there is no further change in assignment of instances to clusters. In this work, we initialize the clusters using instances chosen at random from the dataset. The calculation of K-means clustering is first, specify the cluster category number and the four categories as defined; i.e., the five-level evaluation result will turn into four levels. Second, identify the centers of K initial categories. SPSS can choose a K representative dataset of geo-environmental subfactors as initial category centers based on the actual dataset situation. The Euclidean distance of all datasets to K category centers is calculated as follows: vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u k uX EUCLID ¼ t ðxi yi Þ2 : ð4Þ

M M M M M M M M N M M

i¼1

N N N N N N N N N N M

L L L L L L L L N L L

SPSS classified all datasets of geo-environmental subfactors into the categories where each center locates according to the shortest distance between K category centers, and thus, a new K category is formed. In this study, K ¼4, and the iterative process was finished one by one. The work flow is as follows (Fig. 4). The geo-environmental suitability of the Hangzhou urban construction land was divided into four levels: a construction land suitability zone I suitable for super high-rise and high-rise buildings, a construction land suitability zone II suitable for multistorey buildings, a construction land suitability zone III suitable for low-rise buildings, and construction land suitability zone IV not suitable for buildings.

N, not compatible; L, low compatible; M, moderate compatible; and F, fully compatible.

N N N L N N N L L L F L M F F L M F F F F L F F F F F F F F F F F N N N N N N N N N N L N N N L N N N L N L M L L M F L L M F M M M M M F F M M F F F F F F F F F F F F F F F F Super-high rises High rises Multistorey Low rises Commercial land Super-high rises High rises Multistorey Low rises Industrial land Polluting No polluting Mining sites Residential land

Middle o 21 2–51 5–151 15–251 4 251 High

F F F F F F F F F F F

o 0.5 0.5–1.5 1.5–3.0 4 3.0 Saline Slightly saline Fresh High Middle Low Hardly water water water

Groundwater salinization Capacity building foundation

Low

Poor Fair Good

Back-propagation neural network is a feed-forward connection model formed with multilayer perception. Neurons at the same layer are not connected with each other, but neurons at adjacent layers are connected with the weight. The multilayer backpropagation algorithm, namely the BP algorithm, is most widely used (Rumelhart et al., 1986; Salomon and Hemmen, 1996) for its self-adaptive learning in a given environment. According to the urban geo-environment and construction land characteristics of Hangzhou, the BP neural network model of geo-environmental suitability evaluation of urban construction land was established in this study (Fig. 5). Nine subfactors were chosen for the input layers according to the evaluation factors system. Four neurons in the hidden layer were identified according to the number of predetermined Hangzhou urban construction land levels, and one output neuron was decided according to the predetermined Hangzhou urban construction land suitability level. Therefore, there were nine neurons in the input layer, one neuron in the output layer, and four neurons in the hidden layer in this BP neural network. At the same time, nodes at the same layer are not connected, nodes at different layers form a focus-like connection path, and weight value represents the connection strength between adjacent nodes in this model. In the evaluation process for construction land, the model did not need to understand the relation between input subfactors and output subfactors, as it could ‘‘memorize’’ the information on the sample with its self-learning function. The BP neural network model could automatically search the relations and give mathematical expression to the suitability evaluation according to the data of the training sample. As a result, the BP neural network

Geological hazard degree Groundwater depth (m) Stability of engineering geology

2.7. Based on BP neural network suitability evaluation

Slope

Factors Construction land categories

Table 1 The compatibility between the geological environment factors and construction land in Hangzhou.

Wagstaff et al., 2001). It proceeds by selecting k initial cluster centers and then iteratively refining them as follows:

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Table 2 Construction land suitability evaluation grading standards based on the geo-environment in the study areas. No. Construction grade

9

7

5

3

1

1

Geomorphic type

Plain region

Coastal plain region

o 21 I 0m

2–51 II 0–10 m

4251 IV 430 m

5

Slope Site soil type Holocene saturated soft soil depth Stratum steadiness

Valley area of mountains 15–251 – 20–30 m

–

2 3 4

Middle-low mountainous and hilly region 5–151 III 10–20 m

Tiaoxi River alluvial plain subregion (Renhe County and Pingyao County )

Hard and relatively hard pyroclastic rock subregion, and relatively hard clastic rock subregion, Tiaoxi River alluvial plain subregion (main city area)

Karst Carbonate subregion, Qiantangjiang River alluvial plain subregion, and Tiaoxi River alluvial plain subregion (LinpingTangqi east area)

6

Groundwater salinization

Fresh water

Flood valley deposit in mountainous subregion, and residual slope deposit subregion Saline water

o 100 m3/d

–

41000 m3/d

o 100 m3/d

100–1000 m3/d

1000–3000 m3/d

–

43000 m3/d

–

Bare spring discharge o 1.0 L/S

–

Bare spring discharge – 41.0 L/S, and covered spring discharge 41.0 L/S

–

Spring discharge o 1.0 L/S

–

Spring discharge 41.0 L/S

8

Phreatic water Confined water Groundwater Carbonate abundance rock fissure crave water Bedrock fissure water Geological hazard type

Fresh water above and saline water Saline water above and fresh under water under – 100–1000 m3/d

Soft and hard uneven clastic rock subregion, and Puyangjiang River alluvial plain subregion Slightly saline water

Earthquake

Earthquake

Debris flow, landslide, and surface collapse

9

Geological hazard degree

Hardly

–

Low

Debris flow, Debris flow, landslide, and surface landslide, and surface collapse collapse Middle High

7

–

Table 3 Weights of factors for construction land suitability evaluation based on the geo-environment. Evaluation factors

Weight (%)

Evaluation subfactors

Weight (%)

Geomorphology

25

Engineering geology

45

Hydrological geology

15

Geological hazard

15

Geomorphic type Slope Site soil type Stratum steadiness Holocene saturated soft soil depth Groundwater salinization Groundwater abundance Geological hazard type Geological hazard degree

40 60 10 70 20 40 60 40 60

Total

100

–

–

model established in this study can accurately reflect the urban construction land level. Five hundred and forty-nine training samples were selected randomly in consideration of a balanced regional distribution and a uniform distribution of evaluation factors and levels in order to train the BP network by using Matlab 7.0. The actual output value of the network is compared to the expected output value to calculate whether the error is within the tolerance. If the difference is within the tolerance, the network mapping result is considered the final result; if not, the input and output parameters of the model will be normalized as a group of new training samples and added to the original sample set to have network training again until the error is within tolerance. The optimal assessment of the BP neural network is determined after the network models have repeated debugging training and testing; at the same time, the convergence standard is the fastest and the prediction error is the smallest. After repeating debugging training on the network model with the 549 training samples, the BP neural network with 0.01 step

Percentage (%) 10 15 4.5 31.5 9 6 9 6 9 100

size, 0.001 system accuracy, and 1000 iterations was found to provide the best approximation to the function, the smallest error, and the least network training time. The results of the geo-environment suitability evaluation of the urban construction land of Hangzhou were obtained using the trained BP neural network model to calculate the input characteristics value of 56,332 evaluation units of Hangzhou urban construction land.

3. Results 3.1. Results of suitability evaluation based on K-means clustering and BP neural network Figs. 6 and 7 show the results of the Hangzhou construction land geo-environmental suitability evaluation based on K-means clustering and the BP neural network, respectively.

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Confirm suitability evaluation factor index system for construction land based on geo-environment

Evaluation factors occurrence actuality

Ensure evaluation factors classified standard

Evaluation factors weight

Evaluation factors value

Calculate integrated values for evaluation unit K-Means clustering

Results of geo-environmental construction land suitability evaluation Fig. 4. Flow based on K-means clustering geo-environmental construction land suitability evaluation.

Weight amend

Evaluation factor Geomorphic type Slope Site soil type Holocene saturated soft soil depth Stratum steadiness

Error

Export

Groundwater salinization Groundwater abundance Geological hazard type Geological hazard degree Input layer

Hidden layer

Output layer

Fig. 5. BP neural network model of construction land geo-environmental suitability evaluation.

Construction land geo-environmental suitability zone I has good geological conditions, mainly associated with the lake alluvial plain subregion of Tiaoxi River, with slope o21, saturated soft soil depth less than 10 m, most site soil of grades II and III, saline or slightly saline groundwater. Water inflow is small, and the terrain is not prone to geological disasters. It is mainly distributed in Tangqi District, Liangzhu District, and Gongshu District of the major urban area. This area is surrounded by many scenic sites, and the geological environment is suitable for urbanization and thus has great development potential for super high-rise and high-rise buildings. Construction land geo-environmental suitability zone II has a comparatively strong bearing stratum and relatively good geological conditions, i.e., mostly distributed in the plains region, lake alluvial plains subregion of Tiaoxi River, and ocean alluvial plains subregion of the Qiantangjiang River. The slope is o21, Holocene saturated soft soil depth is about 10–20 m, and site soil of grade III. Except for the freshwater distribution in Yunhe County, the rest of the zone II area is saline water or slightly saline water distribution; the water inflow of Sandun County, Chongxian County, Kangqiao County, and north Hezhuang County is 43000 m3/day, and the water inflow in the rest of the zone II area is low and hardly prone to geological disasters. It is mainly distributed in the plains of major urban areas and in the north and west Yuhang District, including Dingqiao County, Qiaosi County,

Shiqiao County, Shangtang County, Sandun County, Chongxian County, Kangqiao County, and Yunhe County. In addition, it is also sparsely distributed in Guali County, east Dangshan County, and north Hezhuang County in the Xiaoshan District. This area covers a large territory and has great development potential for multistorey buildings. It also conforms to the current land use status and future construction land planning for science and technology development, conferences and trade fairs, high-tech industry, travel, and leisure. Construction land geo-environmental suitability zone III has common geological conditions and has many restrictions for buildings, i.e., mostly distributed in the coastal plains region, the Qiantangjiang River ocean alluvial plain subregion, the Fuyang River lake alluvial plains subregion, the middle-low mountainous and hilly region, and the hard and relatively hard pyroclastic rock subregion. It has a steeper slope from 21 to 51, with distribution of 0–30 m Holocene saturated soft soil depth and site soil grades I, II, and III. Except for the water reservation area, which has fresh water, the rest of the zone III area has generally saline water or slightly saline water, confined and unconfined aquifers fractured rock aquifers with spring discharge o1.0 L/S. It is mainly distributed in the Xianshan District and on both sides of the Qiantangjiang River, especially in the east Xianshan District along the river. In addition, it is also sparsely distributed in the Jingshan Scenic Resort and water reservation areas. The area is only

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Fig. 6. Results of Hangzhou construction land geo-environmental suitability evaluation based on K-means clustering.

Fig. 7. Results of Hangzhou construction land geo-environmental suitability evaluation based on the BP neural network.

suitable for low-rise buildings because the bearing stratum here is generally made up of soft soil. Construction land geo-environmental suitability zone IV is not suitable for construction development because of the steep slopes and poor geological conditions. It is mainly distributed in the southwest hills of the study area and sparsely distributed in the surrounding hills of the West Lake scenic spot and water reservation areas mainly including Baizhang County, Luniao County, Jingshan County, Zhongtai County, Xianlin County, Liuxia County, Longwu County, Zhuantanag County, Lishan County, Heshang County, and Jinhua County.

3.2. Comparison of K-means clustering and the BP neural network Table 4 shows the comparison results of the geo-environmental suitability evaluation results of the Hangzhou construction land based on K-means clustering and the BP neural network. We can see, from Table 4, that the geo-environmental suitability evaluation results of construction land based on K-means clustering and the BP neural network are similar in terms of the distribution and scale of construction land suitability level. Table 4 shows: (1) the area of construction land geo-environmental suitability zone I is 321.74 and 330.12 km2, accounting for 10.48% and

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Table 4 Suitability evaluation results comparison based on K-means clustering and the BP neural network. Model

Suitability level

Suitability zone I

Suitability zone II

Suitability zone III

Suitability zone IV

K-means clustering

Area (km2) Percentage (%) Area (km2) Percentage (%)

321.74 10.48 330.12 10.76

722.81 23.56 692.29 22.56

1412.41 46.04 1447.36 47.18

611.01 19.92 598.23 19.5

BP neural network

Table 5 Comparison analysis between construction land geo-environmental suitability results and distribution of single geo-environmental factors in Hangzhou. Suitability level

Suitability zone I

Geomorphic type

Mainly distributed in Xiaoshan District, Valley area of mountains and middle-low mountainous and hilly the plains on both sides of region. Qiantangjiang River, and also distributed in middle-low mountainous and hilly regions. Zones I and II distributed in plain. Zones III and IV mainly distributed in area of steep slope. Zone IV can be found in the area of Site soil is generally Grades Zone II is generally located in the area Zone III can be found in the area of grade I and II site soil, with poor grades I, II, and III site soil, with poor II and III. with grade III site soil, and sparsely consistency with site soil grade. consistency with site soil grade. distributed in the area with grade II site soil. Various Holocene saturated soft soil Similar to exposed bedrock r 10 m, zone I distribution Most areas are about 0–20 m except depth ranges from 0 to 30 m. distribution. similar to the soft soil depth the north part of Hezhuang county, which is 20–30 m. distribution.

Slope Site soil type

Holocene saturated soft soil depth Stratum steadiness

Suitability zone II

Geological hazard degree

Suitability zone IV

Zones I and II distributed in plain.

Most areas are in Tiaoxi River alluvial plain subregion, zone I distribution similar to engineering condition distribution.

Tiaoxi River alluvial plain subregion and Qiantangjiang River alluvial plain subregion.

Coastal plain region, Qiantangjiang River alluvial plain subregion, Puyangjiang River alluvial plain subregion, middlelow mountainous and hilly regions, hard and relative hard pyroclastic rock subregion, and relative hard clastic rock subregion. All zone III has saline water or slightly saline water distribution, except that water reservation area has freshwater distribution; zone III is consistent with groundwater salinization distribution.

All zones II has distribution of saline water or slightly saline water except that Yunhe county has freshwater distribution; zone II is inconsistent with groundwater salinization distribution. Zone I is consistent with the Zone II is consistent with groundwater Zone III has poor consistency with abundance area. ground water abundance. lower groundwater abundance area. Zone III is generally in the area which is Zone I is in the area which is Most zones II is the area which is barely prone to geological hazards. barely prone to geological hazards, a rarely prone to geological small portion of middle-low hazards. mountainous and hilly region is barely prone to earthquakes.

Groundwater Groundwater is generally salinization saline or slightly saline; zone I is inconsistent with distribution of groundwater salinization. Groundwater abundance

Suitability zone III

10.76% of the total area, respectively, and this result indicates that there is only about 10% area suitable for super high-rise and highrise buildings in Hangzhou; at the same time, we can see that the difference of results is the smallest between the K-means clustering and the BP neural network for construction land geo-environmental suitability zone I, which is only 0.28%; (2) the area of construction land geo-environmental suitability zone II is 722.81 and 692.29 km2, accounting for 23.56% and 22.56% of the total area, respectively, and this result indicates that there is about 23% area suitable for multistorey buildings; (3) the area of construction land geo-environmental suitability zone III is 1412.41 and 1447.36 km2, accounting for 46.04% and 47.18% of the total area, based on K-means clustering and the BP neural network, respectively; and this result indicates that most of the area in Hangzhou is suitable for low-rise buildings; we can see that the difference of results is the biggest between the K-means clustering and the BP neural network for construction land geo-environmental suitability zone III, which is 1.14%; (4) the area of construction land geoenvironmental suitability zone IV is 611.01 and 598.23 km2, accounting for 19.92% and 19.5% of the total area, respectively, and this result indicates that there is about 20% area not suitable

Flood deposits of valley area in mountainous subregion, flood alluvial plain subregion in river valley, and eroded and abrased clastic rock deposit in middle-low mountainous and hilly subregions. Groundwater is mostly fresh water.

Zone IV is consistent with distribution of groundwater abundance. Easily prone to geological hazards, including distribution in areas highly, middle, and barely prone to geological hazards.

for buildings in Hangzhou. Table 4 also demonstrates that most urban areas of Hangzhou are suitable for construction land development. In addition, the evaluation results are consistent with current land use. This is also confirmation that the current land use structure is more reasonable in Hangzhou (Zhang et al., 2009). The differences of evaluation results are little between the K-means clustering and the BP neural network from the above comparison. Further field investigation shows that both methods are effective for construction land suitability evaluation in Hangzhou 3.3. Comparison between evaluation results and distribution of geoenvironmental factors The distribution of different construction land geo-environmental suitability levels is closely related to the distribution of single geo-environmental factors. Their consistency reflects the significant influence of leading factors, while the difference reflects the complementary role of other relative factors. The distribution chart of the geo-environmental suitability evaluation results for construction land is compared with the distribution chart for a single geoenvironmental factor, and their relationship is shown in Table 5.

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Table 5 demonstrates that the geo-environmental suitability evaluation results for the urban construction land of Hangzhou are consistent with the distribution of geo-environmental conditions, particularly highly consistent with geomorphic type, slope, engineering geological lithology, Holocene saturated soft soil depth, stratum steadiness, bearing stratum, groundwater abundance, and geological hazard degree; but barely consistent with site soil type and groundwater salinization. It can be concluded that the results of the evaluation of Hangzhou urban construction land geo-environmental suitability based on the BP neural network are from the integrative action of leading factors along with other complementary factors.

4. Discussion Geo-environmental suitability evaluation of urban construction land is a comprehensive evaluation and involves many influencing factors that interact with each other. Because of the complicated spatial variability of various factors and subfactors, processing by human beings is arbitrary and subjective, and the work is difficult and time consuming. But the application of GIS, grid analysis, K-means clustering, and the BP neural network in the geoenvironmental suitability evaluation of urban construction land can help reduce human errors in data processing. At the same time, results also showed that the real advantage of the methods proposed in this paper lies in their capacity to streamline the mapping process and to ensure that the results are consistent throughout (as determined by the physical criteria and their weighing factors). Factors influencing geo-environmental suitability evaluation are a complicated system, and classification criteria of evaluation indices directly impact the evaluation results. Therefore, the classification should be defined by the features of each study area. Different evaluation methods will lead to different evaluation results even for the same area and with the same factors selected. As a result, model establishment is the core of the study and will directly impact the geo-environmental suitability evaluation results for the urban construction land of Hangzhou. The two models established in the study take a grid cell as an evaluation unit, take full advantage of various existing geological survey results of Hangzhou, and comprehensively consider various geo-environmental factors influencing Hangzhou urban construction land use. The two methods have their own unique features. Suitability evaluation based on K-means clustering is simple and fast in calculation, and its key problem is the weight definition of each factor and subfactor. But it has shortcomings such as being vulnerable to the impact of the initial cluster conditions, easily falling into the local minimum. Suitability evaluation based on the BP neural network involves few human elements, and its key problem is in dataset selection. Compared with the classic maximum-likelihood method, the biggest advantage of the BP neural network is that there is no requirement for a normal distribution of a training sample. At the same time, in the evaluation process for construction land, the model does not need to understand the relation between input subfactors and output subfactors, as it can ‘‘memorize’’ the information on the sample with its self-learning function. The BP neural network model could automatically search the relations and give mathematical expression to the suitability evaluation according to the data of the training datasets. However, the model does have disadvantages such as a hard-to-decide structure and being easily subject to local minimum, which makes it difficult to achieve a calculation convergence. The results of the two evaluation methods are consistent with the variability of the suitability level, engineering geology, and hydrogeology of the plains region. On the one hand, evaluation can help make full use of the land resources represented by

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environmental conditions, while on the other hand, through minimizing the restrictions on environmental conditions, evaluation can help to optimize the allocation of urban construction, geological resources, and environment. In this way, a safe, reasonable, and economic urban construction can be reached, providing a scientific basis for decision-making in the urban development and construction land development of Hangzhou. However, geo-environmental suitability evaluation factors influencing urban construction land are complicated. Therefore, whatever mathematical methods are adopted for geo-environmental suitability evaluation of urban construction land, we must make it clear that evaluation factors, factor grading, index assignment, and factor weight are critical to the evaluation results. A scientific and objective evaluation conclusion is possible only when each evaluation step and method is properly processed.

Acknowledgments This work has been supported from the Special Fund for Basic Scientific Research of Central Colleges (No. CUGL090232), Key Lab of Biogeology and Environmental Geology of Ministry of Education (No. BGEG1014), China University of Geosciences, Wuhan; Hangzhou Land Use Suitability Research, Subproject of Hangzhou Urban Geology Survey of Welfare Geology Survey Project Funded by National Geological Bureau (No. 200413000021); and the Fund for Geological Survey of China Geological Survey (No. 1212010880404). The authors thank the anonymous reviewers for providing valuable comments on the manuscript.

Appendix A. Supplementary material Supplementary data associated with this article can be found in the online version at doi:10.1016/j.cageo.2011.03.006.

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