Factors Influencing Regional-Scale Wildfire Probability in Iran

28 Factors Influencing Regional-Scale Wildfire Probability in Iran: An Application of Random Forest and Support Vector Machine Abolfazl Jaafari1, Hamid Reza Pourghasemi2 1

RESEARCH I NSTITUTE OF FORESTS AND RANG ELANDS, AGRICULTURAL R ESEARCH, EDUCATION AND EXTENSION ORGANIZATION (AREEO), TEHRAN, IRAN 2 DEPART ME NT OF NATURAL R ESOURCES AND ENVIRONMENTAL ENGINEERING, COLLEGE OF AGRICUL TURE, SHIRAZ UNIVERSITY, SHIRAZ , IRAN

28.1 Introduction An accurate estimate of fire probability plays a vital role in reducing the negative effects of wildfires (Fischer et al., 2016; North et al., 2015; Parisien et al., 2012). There is evidence that improvement in assessing wildfire probability, delimiting the landscape into different probability levels, and identifying the effects of different landscape characteristics on wildfire occurrence help inform managers and policymakers to adopt precautionary measures and policies for fireprone landscapes (Mhawej, Faour, Abdallah, & Adjizian-Gerard, 2016; Stephens et al., 2013). The availability of spatially explicit information of the effects of different landscape characteristics on wildfire occurrence is a significant step toward predicting fire probabilities and developing predictive models to quantify the impact of climate changes, human activities, and fire ignitions on terrestrial ecosystems. The wildfire causative factors are often categorized into four main categories: topography, climate, vegetation, and human activities (Ganteaume et al., 2013; Nami, Jaafari, Fallah, & Nabiuni, 2018; Parisien et al., 2012). The effect of topographic factors (e.g., slope, aspect, and elevation) on fire ignition is largely indirect (Jaafari, Gholami, & Zenner, 2017; Parisien et al., 2012) by influencing vegetation, local climate, and human accessibility (Jaafari & Mafi Gholami, 2017; Nami et al., 2018). Climate factors (e.g., rainfall, temperature, wind, and evapotranspiration) exert both direct and indirect influences on wildfire occurrence (Jaafari, Gholami, et al., 2017; Nami et al., 2018; Parisien et al., 2012). Vegetation (i.e., land cover) effects on fire ignition and spread through fuel characteristics such as type, load, and moisture content (Adab, Kanniah, & Solaimani, 2013; Adab, Kanniah, Solaimani, & Sallehuddin, 2015; Nami et al., 2018). Humans affect the Spatial Modeling in GIS and R for Earth and Environmental Sciences. DOI: https://doi.org/10.1016/B978-0-12-815226-3.00028-4 © 2019 Elsevier Inc. All rights reserved.

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spatial pattern and frequency of fire occurrence by altering natural vegetation and providing ignition sources in ways that may either promote or limit fire (Abdi, Kamkar, Shirvani, Teixeira da Silva, & Buchroithner, 2018; Adab et al., 2013). The determination of the relative importance of a given wildfire causative factor with respect to other factors in relation to historical fire events is a highly subjective exercise that usually involves the use of trial-and error procedures (Jaafari, Gholami, et al., 2017), application of different methodologies (Pourtaghi, Pourghasemi, Aretano, & Semeraro, 2016), and refers to expert knowledge (Pourghasemi, Beheshtirad, & Pradhan, 2016). However, over large-extent landscapes and more typically in rugged terrains, there is almost always a shortage of knowledge about the influence of every geo-environmental characteristic, which may lead to low accuracy in the prediction of wildfires (Jaafari, Gholami, et al., 2017; Pourtaghi et al., 2016). Therefore, the determination of the importance geo-environmental factors is a recurrent challenge in the prediction of wildfires. At the same time, this challenge has opened up an avenue for new analyses that can aid in planning more efficient field surveys and the handling of large datasets, and in gaining a better understanding of the intrinsic nature of the landscape characteristics. This study was aimed at investigating the roles of different landscape characteristics on wildfire occurrence and its spatial distribution over a fire-prone landscape in the Zagros Mountains, Iran. In this study, the random forests (RFs) model was utilized to link historical fire events to a set of wildfire causative factors to measure the importance of each factor on fire ignition. In addition, a state-of-the-art data-mining model, that is, support vector machines (SVMs), was employed to produce an accurate estimate of wildfire probability across the study area. Finally, the receiver operating characteristic (ROC)
AUC method was used for the assessment and validation of the results.

28.2 Study Area The study area is the Chaharmahal
Bakhtiari Province, located in the Zagros Mountains region of Iran (Fig. 28-1). The province has an area of 16,532 km2, where 77% of the territory is covered with forests and rangelands (Jaafari, Zenner, & Pham, 2018). The local climate in the study area is controlled by its rugged topography. The mean precipitation is 650 mm/ year, falling mostly as snow during autumn and winter, and rain during spring. The average summer and winter temperatures are recorded as 24 and 10 C, respectively. In this province, the fire season typically extends from June until October with a single modal seasonal distribution that peaks in July and August (Jaafari, Gholami, et al., 2017).

28.3 Materials and Methods To determine the relative importance of wildfire causative factors and to estimate the spatial variability of wildfire probability in the study area, a three-step methodology was adopted as follows: (1) data collection and processing, (2) factor analysis, and (3) spatial modeling and validation.

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FIGURE 28-1 Location of study area and historical fire events.

28.3.1 Data Collection and Processing To prepare the wildfire causative factors used in this study, the recommendations given in the corresponding literature (e.g., Adab et al., 2013, 2015; Catry, Rego, Bação, & Moreira, 2009; Chen et al., 2015; Jaafari et al., 2018; Nami et al., 2018; Oliveira, Oehler, San-MiguelAyanz, Camia, & Pereira, 2012; Pourtaghi et al., 2016; Tien Bui et al., 2017; Tien Bui, Le, Nguyen, Le, & Revhaug, 2016) were followed. In the literature, the number of causative factors ranges from only a few (e.g., Leuenberger, Parente, Tonini, Pereira, & Kanevski, 2018) to several (e.g., Adab et al., 2013, 2015; Jaafari et al., 2018; Pourtaghi et al., 2016), depending on the scope of study, availability of data, and the relevance with respect to fire causes. Although landscape characteristics act jointly and the exclusion of two or three variables

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may lead to a decrease in the predictive accuracy (Jaafari, Gholami, et al., 2017), in a geographic information system (GIS)-based modeling study, the selected factors should be operational, complete, nonuniform, measurable, and nonredundant (Jaafari, Najafi, Pourghasemi, Rezaeian, & Sattarian, 2014). Thus, in this study, nine potential causative factors were selected: slope degree, aspect, altitude, mean annual temperature and rainfall, wind effect, and proximity to settlements, rivers, and roads (Fig. 28-2). The map of all of these factors was constructed in raster format with a pixel size of 50 3 50 m. Even though a finer resolution could have been adopted, the spatial resolution was limited by the resolution of topographic data and computational resources. To perform a spatially explicit modeling of wildfire probability, an inventory map of fire events that have occurred in the recent past was prepared. The inventory map of historical fire locations was compiled using the moderate resolution imaging spectroradiometer (MODIS) hot spot products (http://earthdata.nasa.gov/firms), the archive provided by the administrative office of natural resources of the Chaharmahal
Bakhtiari Province, and multiple field surveys. In total, 132 fire events were detected and mapped as geo-referenced polygons from the period 2007
14. The fire events were then randomly divided into two groups: the first group comprised 70% of the events (92 fires comprising 1096 fire grid cells) and was used for the training of the probability model; the second group with 30% of the events (40 fires comprising 470 fire grid cells) was used for validation of the model (Nami et al., 2018; Pourghasemi, 2016; Pourtaghi et al., 2016; Jaafari, Gholami, et al., 2017; Jaafari, Rezaeian, & Omrani, 2017).

FIGURE 28-2 Wildfire causative factors used in this study.

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28.3.2 Factor Analysis Using the Random Forest Model The RF model, proposed by Breiman (2001), is an ensemble learning technique for classification and regression. This model creates several decision trees that are aggregated to perform a classification task, to select important variables, and to calculate the relative importance of each variable (Breiman, Friedman, Stone, & Olshen, 1984). Each tree in the forest is built on about two-thirds of the input data, while the remaining third of the data [i.e., the OOB (out-of-bag) data] is retained for model validation. During the modeling process, RF estimates the importance of factors using the Gini impurity criterion. Gini importance is computed by randomly permuting the values of factor m in the OOB cases and putting these cases down the tree, while keeping all others unchanged (Liaw & Wiener, 2002). In the R statistical package, RF has two parameters that affect the performance of the model, that is, the number of trees T and the number of environmental covariates in each random subset M, which were found to be 1000 and 3, respectively, for best performance in this study. The computational process was carried out using the free “random-forest” package available in the R statistical software.

28.3.3 Probability Modeling Using the Support Vector Machine Model The SVM model, proposed by Vapnik (1995), is one of the soft computing learning algorithms which has been widely used in different fields of science (Pham, Jaafari, Prakash, & Bui, 2018; Pourghasemi & Rahmati, 2018; Rodrigues & de la Riva, 2014; Tehrany, Pradhan, Mansor, & Ahmad, 2015). This model uses a statistical learning theory and the structural risk minimization principle to separate two classes (e.g., fire and nonfire) with a linear hyperplane (Kavzoglu, Sahin, & Colkesen, 2014). This algorithm reshapes the nonlinear world into the linear by generating a separating hyperplane (Tehrany et al., 2015). An optimum separating hyperplane can be obtained by solving the following classification function (Vapnik, 1995): f ðνÞ 5 sgn

n X

! αi Li K ðv; vn1 Þ 1 b

(28-1)

i51

where n denotes the number of explanatory variables, αi is the Lagrange multiplier, v and L are, respectively, the vectors of explanatory variables (i.e., causative factors) and fire labels (i.e., fire or nonfire), b is the constant value, and K(v,vi) is the kernel function that can be selected as linear, polynomial, radial basis function (RBF), or sigmoid. In the case of the binary classifications (e.g., fire modeling), the condition for solving Eq. (28-1) is assumed as follows (Pham, Pradhan, Bui, Prakash, & Dholakia, 2016):  t   ω ϕðvi Þ 1 b $ 1; if Li ωt ϕðvi Þ 1 b $ 13 ωt ϕðvi Þ 1 b # 1; if

Li 51 1ðfireÞ Li 5 2 1ðnon-fireÞ

(28-2)

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where ϕðvi Þ is a nonlinear function that divides the input space into higher dimension space, and ϕ represents the weight vector. In this study, the nonlinear RBF kernel function, which can effectively transform the nonlinear classes into a linear one in high dimensional space, was used (Pourghasemi, Yousefi, Kornejady, & Cerdà, 2017). To fit the SVM model using the nonlinear kernel, the kernlab package and the ksvm function of the R statistical software were employed (Karatzoglou et al., 2007).

28.3.4 Probability Mapping and Validation Following successful training of the SVM model, the model was used to estimate the value of wildfire probability for each pixel in the study area. The values were then classified and grouped using the natural breaks method (Hong et al., 2018; Pourtaghi et al., 2016) into the relative levels of low, moderate, high, and very high probability of wildfire occurrence. The produced probability map was evaluated by the area under the ROC curve, known as the ROC
AUC method, which illustrated both the success rate and prediction rate curves (Pourtaghi et al., 2015). The success rate that uses the training dataset indicates how well the modeling results fit the training dataset. The prediction rate that uses the validation dataset measures how well the model predicts future fires across the landscape (Jaafari, Rezaeian, et al., 2017). Both success and prediction rates can range from 0 to 1 and values of ,0.6 indicate a poor, 0.6
0.7 a moderate, 0.7
0.8 a good, 0.8
0.9 a very good, and .0.9 an excellent model performance (Hosmer, Lemeshow, & Sturdivant, 2013).

28.4 Results and Discussion 28.4.1 Factor Importance The OOB rate derived from the RF model showed an accuracy rate of 70.11%, which indicates a reasonably good model performance for classifying nonfire and fire pixels over the study area (Pourghasemi & Kerle, 2016; Pourtaghi et al., 2016). The confusion matrix (Table 28-1) that recorded the disagreement between model predictions and actual outcomes of the training dataset showed that the RF model correctly classified 70.7% and 69.6% of the nonfire and fire pixels, respectively. The final outcome of the RF model was a rank of relative importance of wildfire causative factors using two importance measure (i.e., mean decrease accuracy and mean decrease Gini) (Fig. 28-3 and Table 28-2). Given that higher values of these measures indicate a higher Table 28-1 1 5 Fire) 0 1

Confusion Matrix From the RF Model (0 5 Nonfire, 0

1

Overall Class Error

65 28

27 64

0.293 0.304

OOB estimate of error rate: 29.89%; model accuracy: 70.11%. OOB, Out-of-bag; RF, random forest.

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FIGURE 28-3 Two measures of factor importance calculated by the RF model. RF, Random forest.

Table 28-2 Relative Importance of Wildfire Causative Factors Extracted by the RF Model (0 5 Nonfire, 1 5 Fire)

Proximity to settlements Rainfall Altitude Proximity to roads Temperature Wind effect Slope degree Proximity to rivers Aspect

0

1

Mean Decrease Accuracy

Mean Decrease Gini

4.94 9.61 1.27 2.42 2 4.28 5.36 5.21 2 1.99 2 5.90

25.02 17.98 18.41 18.47 12.19 3.41 1.68 4.37 2 1.11

21.01 19.79 15.76 15.52 7.28 6.33 4.65 1.94 2 4.57

9.38 8.85 7.41 7.40 5.31 4.84 5.13 5.27 4.73

RF, Random forest.

level of importance for a specific causative factor (Pourtaghi et al., 2016), proximity to human settlements (accuracy 5 20.01; Gini 5 9.38), annual rainfall (accuracy 5 19.79; Gini 5 8.85), altitude (accuracy 5 15.76; Gini 5 7.41), and proximity to roads (accuracy 5 15.52; Gini 5 7.40) were identified as the most important wildfire causative factors. These results clearly show that the human-related factors (i.e., proximity to settlements, roads, and rivers) were significantly stronger than climate- (i.e., rainfall, temperature, and wind effect) and topographic-related (i.e., slope, aspect, and altitude) factors. Human-related factors jointly accounted for 38.46 and 22.05 in terms of accuracy and Gini measures, respectively. This result is in agreement with other findings that reported the significance of human activities for fire occurrence (e.g., Achard, Eva, Mollicone, & Beuchle, 2008; Ganteaume et al., 2013; Nami et al., 2018; Syphard et al., 2007; Yang, He, Shifley, & Gustafson, 2007). Further, the

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results demonstrated the significance of climate factors in spatial distribution of wildfire probability across the study area. These results are supported by previous findings that highlighted the strong influence of climate factors and ongoing climate changes on the intensity and frequency of wildfires (e.g., Flannigan, Stocks, Turetsky, & Wotton, 2009; Gillett, Weaver, Zwiers, & Flannigan, 2004; Stocks et al., 1998; Wu, He, Yang, Liu, & Liang, 2014).

28.4.2 Prediction Map The application of the SVM model resulted in distribution maps that represent the different levels of probabilities of fire ignition across the study area (Fig. 28-4). A comparison between the

FIGURE 28-4 Distribution map of wildfire probability produced using the SVM model. SVM, Support vector machine.

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four probability levels delimited by the SVM model suggests that the high and very high probability levels cover almost 50% of the study area. Thus, fire-fighting efforts and resources/infrastructure should be directed to roughly half of the area of Chaharmahal
Bakhtiari Province. The validation process using the ROC
AUC method illustrated the success and prediction rates of the SVM model (Figs. 28-5 and 28-6). The ROC curve revealed AUC values of 0.814 for success rate and 0.751 for prediction rate, indicating a reliable model performance to estimate wildfire probability across the research landscape. More precisely, the SVM

FIGURE 28-5 Success rate curve of the SVM model. SVM, Support vector machine.

FIGURE 28-6 Prediction rate curve of the SVM model. SVM, Support vector machine.

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model was more successful in dealing with the training dataset and correctly classified 83.70% of fire pixels (5sensitivity) and 74.48% of nonfire pixels (5specificity). However, its performance decreased slightly in the validation phase (i.e., predicting future fires) and correctly classified 75% of fire pixels (5sensitivity) and 72.5% of nonfire pixels (5specificity). When the results of the SVM model are compared to what has been reported in previous works, it can be seen that the results are quite different according to research areas. For instance, while Tien Bui et al. (2016, 2017) reported AUC values of 0.88% for the prediction rate of SVM in tropical areas of Vietnam, Rodrigues and de la Riva (2014) achieved an AUC value of 0.71 in Spain, indicating the need for further applications of SVM in wildfire prediction.

28.5 Conclusion Wildfires are one of the major catastrophic phenomena that occur in territorial ecosystems. Regional- and, perhaps more importantly, national-scale wildfire probability mapping is urgently needed to effectively manage and monitor wildfires to reduce the severe loss of human life and property. In this study, the analysis was performed in a regional spatial extent with a resolution of 50 m and it was found that the probability of a fire is strongly dependent upon the human infrastructure and its associated activities. Findings from the RF model demonstrated that proximity to human settlements, rainfall, altitude, and proximity to roads were the most effective factors on wildfire occurrences. The SVM model that delineated the research landscape to different levels of probabilities to fire occurrences was proven to be effective in predicting future fires with satisfactory accuracy. The insights obtained from this research can be applied to spatially explicit assessment of fire-prone landscapes and to gain a better understanding of the nature of the different landscape characteristics.

Acknowledgment We thank Dr. Davood Mafi-Gholami and the administrative office of the natural resources of the Chaharmahal
Bakhtiari Province, Iran. for providing us with the data used in this study.

References Abdi, O., Kamkar, B., Shirvani, Z., Teixeira da Silva, J. A., & Buchroithner, M. F. (2018). Spatial-statistical analysis of factors determining forest fires: A case study from Golestan, Northeast Iran. Geomatics, Natural Hazards and Risk, 9(1), 267
280. Achard, F., Eva, H. D., Mollicone, D., & Beuchle, R. (2008). The effect of climate anomalies and human ignition factor on wildfires in Russian boreal forests. Philosophical Transactions of the Royal Society B: Biological Sciences, 363(1501), 2329
2337. Adab, H., Kanniah, K. D., & Solaimani, K. (2013). Modeling forest fire risk in the northeast of Iran using remote sensing and GIS techniques. Natural Hazards, 65(3), 1723
1743. Adab, H., Kanniah, K. D., Solaimani, K., & Sallehuddin, R. (2015). Modelling static fire hazard in a semi-arid region using frequency analysis. International Journal of Wildland Fire, 24(6), 763
777. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5
32.

Chapter 28 • Factors Influencing Regional-Scale Wildfire Probability in Iran

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Breiman, L., Friedman, J., Stone, C. J., & Olshen, R. A. (1984). Classification and regression trees. Boca Raton, FL: CRC Press. Catry, F. X., Rego, F. C., Bação, F. L., & Moreira, F. (2009). Modeling and mapping wildfire ignition risk in Portugal. International Journal of Wildland Fire, 18(8), 921
931. Fischer, A. P., Spies, T. A., Steelman, T. A., Moseley, C., Johnson, B. R., Bailey, J. D., . . . Kline, J. D. (2016). Wildfire risk as a socioecological pathology. Frontiers in Ecology and the Environment, 14(5), 276
284. Flannigan, M., Stocks, B., Turetsky, M., & Wotton, M. (2009). Impacts of climate change on fire activity and fire management in the circumboreal forest. Global Change Biology, 15(3), 549
560. Ganteaume, A., Camia, A., Jappiot, M., San-Miguel-Ayanz, J., Long-Fournel, M., & Lampin, C. (2013). A review of the main driving factors of forest fire ignition over Europe. Environmental Management, 51(3), 651
662. Gillett, N. P., Weaver, A. J., Zwiers, F. W., & Flannigan, M. D. (2004). Detecting the effect of climate change on Canadian forest fires. Geophysical Research Letters, 31(18), 1
4. Hong, H., Tsangaratos, P., Ilia, I., Liu, J., Zhu, A. X., & Xu, C. (2018). Applying genetic algorithms to set the optimal combination of forest fire related variables and model forest fire susceptibility based on data mining models. The case of Dayu County, China. Science of the Total Environment, 630, 1044
1056. Hosmer, D. W., Jr, Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression (Vol. 398). Hoboken, NJ: John Wiley & Sons. Jaafari, A., Gholami, D. M., & Zenner, E. K. (2017). A Bayesian modeling of wildfire probability in the Zagros Mountains, Iran. Ecological Informatics, 39, 32
44. Jaafari, A., & Mafi Gholami, D. (2017). Wildfire hazard mapping using an ensemble method of frequency ratio with Shannon’s entropy. Iranian Journal of Forest and Poplar Research, 25(2), 232
243. Jaafari, A., Najafi, A., Pourghasemi, H. R., Rezaeian, J., & Sattarian, A. (2014). GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran. International Journal of Environmental Science and Technology, 11(4), 909
926. Jaafari, A., Rezaeian, J., & Omrani, M. S. O. (2017). Spatial prediction of slope failures in support of forestry operations safety. Croatian Journal of Forest Engineering: Journal for Theory and Application of Forestry Engineering, 38(1), 107
118. Jaafari, A., Zenner, E. K., & Pham, B. T. (2018). Wildfire spatial pattern analysis in the Zagros Mountains, Iran: A comparative study of decision tree based classifiers. Ecological Informatics, 43, 200
211. Karatzoglou, A., Smola, A., Hornik, K., Karatzoglou, M.A., Sparsem, S., & Yes, L. (2007). The kernlab package. In: Kernel-based machine learning lab. R package version 0.9.-22. Available online: ,https://cran.r-project.org/web/packages/kernlab.. Kavzoglu, T., Sahin, E. K., & Colkesen, I. (2014). Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression. Landslides, 11(3), 425
439. Leuenberger, M., Parente, J., Tonini, M., Pereira, M. G., & Kanevski, M. (2018). Wildfire susceptibility mapping: Deterministic vs. stochastic approaches. Environmental Modelling & Software, 101, 194
203. Liaw, A., & Wiener, M. (2002). Classification and regression by Random Forest. R News, 2(3), 18
22. Mhawej, M., Faour, G., Abdallah, C., & Adjizian-Gerard, J. (2016). Towards an establishment of a wildfire risk system in a Mediterranean country. Ecological Informatics, 32, 167
184. Nami, M. H., Jaafari, A., Fallah, M., & Nabiuni, S. (2018). Spatial prediction of wildfire probability in the Hyrcanian ecoregion using evidential belief function model and GIS. International Journal of Environmental Science and Technology, 15(2), 373
384. North, M. P., Stephens, S. L., Collins, B. M., Agee, J. K., Aplet, G., Franklin, J. F., & Fulé, P. Z. (2015). Reform forest fire management. Science, 349(6254), 1280
1281.

618

SPATIAL MODELING IN GIS AND R FOR EARTH AND ENVIRONMENTAL SCIENCES

Oliveira, S., Oehler, F., San-Miguel-Ayanz, J., Camia, A., & Pereira, J. M. (2012). Modeling spatial patterns of fire occurrence in Mediterranean Europe using Multiple Regression and Random Forest. Forest Ecology and Management, 275, 117
129. Parisien, M. A., Snetsinger, S., Greenberg, J. A., Nelson, C. R., Schoennagel, T., Dobrowski, S. Z., & Moritz, M. A. (2012). Spatial variability in wildfire probability across the western United States. International Journal of Wildland Fire, 21(4), 313
327. Pham, B. T., Jaafari, A., Prakash, I., & Bui, D. T. (2018). A novel hybrid intelligent model of support vector machines and the MultiBoost ensemble for landslide susceptibility modeling. Bulletin of Engineering Geology and the Environment, 1
22. Pham, B. T., Pradhan, B., Bui, D. T., Prakash, I., & Dholakia, M. B. (2016). A comparative study of different machine learning methods for landslide susceptibility assessment: A case study of Uttarakhand area (India). Environmental Modelling & Software, 84, 240
250. Pourghasemi, H. R. (2016). GIS-based forest fire susceptibility mapping in Iran: A comparison between evidential belief function and binary logistic regression models. Scandinavian Journal of Forest Research, 31 (1), 80
98. Pourghasemi, H. R., Beheshtirad, M., & Pradhan, B. (2016). A comparative assessment of prediction capabilities of modified analytical hierarchy process (M-AHP) and Mamdani fuzzy logic models using Netcad-GIS for forest fire susceptibility mapping. Geomatics, Natural Hazards and Risk, 7(2), 861
885. Pourghasemi, H. R., & Kerle, N. (2016). Random forests and evidential belief function-based landslide susceptibility assessment in Western Mazandaran Province, Iran. Environmental Earth Sciences, 75(3), 185. Pourghasemi, H. R., & Rahmati, O. (2018). Prediction of the landslide susceptibility: Which algorithm, which precision? Catena, 162, 177
192. Pourghasemi, H. R., Yousefi, S., Kornejady, A., & Cerdà, A. (2017). Performance assessment of individual and ensemble data-mining techniques for gully erosion modeling. Science of the Total Environment, 609, 764
775. Pourtaghi, Z. S., Pourghasemi, H. R., Aretano, R., & Semeraro, T. (2016). Investigation of general indicators influencing on forest fire and its susceptibility modeling using different data mining techniques. Ecological Indicators, 64, 72
84. Rodrigues, M., & de la Riva, J. (2014). An insight into machine-learning algorithms to model human-caused wildfire occurrence. Environmental Modelling & Software, 57, 192
201. Stephens, S. L., Agee, J. K., Fulé, P. Z., North, M. P., Romme, W. H., Swetnam, T. W., & Turner, M. G. (2013). Managing forests and fire in changing climates. Science, 342(6154), 41
42. Stocks, B. J., Fosberg, M. A., Lynham, T. J., Mearns, L., Wotton, B. M., Yang, Q., . . . McKenney, D. W. (1998). Climate change and forest fire potential in Russian and Canadian boreal forests. Climatic Change, 38(1), 1
13. Syphard, A. D., Radeloff, V. C., Keeley, J. E., Hawbaker, T. J., Clayton, M. K., Stewart, S. I., & Hammer, R. B. (2007). Human influence on California fire regimes. Ecological Applications, 17(5), 1388
1402. Tehrany, M. S., Pradhan, B., Mansor, S., & Ahmad, N. (2015). Flood susceptibility assessment using GISbased support vector machine model with different kernel types. Catena, 125, 91
101. Tien Bui, D., Bui, Q. T., Nguyen, Q. P., Pradhan, B., Nampak, H., & Trinh, P. T. (2017). A hybrid artificial intelligence approach using GIS-based neural-fuzzy inference system and particle swarm optimization for forest fire susceptibility modeling at a tropical area. Agricultural and Forest Meteorology, 233, 32
44. Tien Bui, D., Le, K. T. T., Nguyen, V. C., Le, H. D., & Revhaug, I. (2016). Tropical forest fire susceptibility mapping at the cat Ba national park area, Hai Phong city, Vietnam, using GIS-Based kernel logistic regression. Remote Sensing, 8(4), 347. Vapnik, V. N. (1995). The nature of statistical learning theory. New York: Springer-Verlag.

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Wu, Z., He, H. S., Yang, J., Liu, Z., & Liang, Y. (2014). Relative effects of climatic and local factors on fire occurrence in boreal forest landscapes of northeastern China. Science of the Total Environment, 493, 472
480. Yang, J., He, H. S., Shifley, S. R., & Gustafson, E. J. (2007). Spatial patterns of modern period human-caused fire occurrence in the Missouri Ozark Highlands. Forest Science, 53(1), 1
15.

Further Reading Robinne, F. N., Parisien, M. A., & Flannigan, M. (2016). Anthropogenic influence on wildfire activity in Alberta, Canada. International Journal of Wildland Fire, 25(11), 1131
1143. Ruffault, J., & Mouillot, F. (2017). Contribution of human and biophysical factors to the spatial distribution of forest fire ignitions and large wildfires in a French Mediterranean region. International Journal of Wildland Fire, 26(6), 498
508.