We used three state-of-the-art machine learning techniques (boosted regression tree, random forest, and support vector machine) to produce a multi-hazard (MHR) map illustrating areas susceptible to flooding, gully erosion, forest fires, and earthquakes in Kohgiluyeh and Boyer-Ahmad Province, Iran. The earthquake hazard map was derived from a probabilistic seismic hazard analysis. The mean decrease Gini (MDG) method was implemented to determine the relative importance of effective factors on the spatial occurrence of each of the four hazards. Area under the curve (AUC) plots, based on a validation dataset, were created for the maps generated using the three algorithms to compare the results. The random forest model had the highest predictive accuracy, with AUC values of 0.994, 0.982, and 0.885 for gully erosion, flooding, and forest fires, respectively. Approximately 41%, 40%, 28%, and 3% of the study area are at risk of forest fires, earthquakes, floods, and gully erosion, respectively.
Considering the large number of natural disasters on the planet, many areas in the world are at risk of these hazards; therefore, providing an integrated map as a guide map for multiple natural hazards can be applied to save human lives and reduce financial losses. This study designed a multi-hazard map for three important hazards (earthquakes, floods, and landslides) to identify endangered areas in Kermanshah province located in western Iran using ensemble SWARA-ANFIS-PSO and SWARA-ANFIS-GWO models. In the first step, flood and landslide inventory maps were generated to identify at-risk areas. Then, the occurrence places for each hazard were divided into two groups for training susceptibility models (70%) and testing the models applied (30%). Factors affecting these hazards, including altitude, slope aspect, slope degree, plan curvature, distance to rivers, distance to roads, distance to the faults, rainfall, lithology, and land use, were used to generate susceptibility maps. The SWARA method was used to weigh the subclasses of the influencing factors in floods and landslides. In addition, a peak ground acceleration (PGA) map was generated to investigate earthquakes in the study area. In the next step, the ANFIS machine learning algorithm was used in combination with PSO and GWO meta-heuristic algorithms to train the data, and SWARA-ANFIS-PSO and SWARA-ANFIS-GWO susceptibility maps were separately generated for flood and landslide hazards. The predictive ability of the implemented models was validated using the receiver operating characteristics (ROC), root mean square error (RMSE), and mean square error (MSE) methods. The results showed that the SWARA-ANFIS-PSO ensemble model had the best performance in generating flood susceptibility maps with ROC = 0.936, RMS = 0.346, and MSE = 0.120. Furthermore, this model showed excellent results (ROC = 0.894, RMS = 0.410, and MSE = 0.168) for generating a landslide map. Finally, the best maps and PGA map were combined, and a multi-hazard map (MHM) was obtained for Kermanshah Province. This map can be used by managers and planners as a practical guide for sustainable development.
Protection against natural hazards is vital in land-use planning, especially in high-risk areas. Multi-hazard susceptibility maps can be used by land-use manager to guide urban development, so as to minimize the risk of natural disasters. The objective of the present study was to use ve machines based on learning methods to produce multi-hazard susceptibility maps in Khuzestan Province, Iran. The rst step in the study was to create four different natural hazards ( oods, landslides, forest res, and earthquakes) using support vector machine (SVM), boosted regression tree (BRT), random forest (RF), maximum entropy (MaxEnt), and learning-ensemble techniques. Effective factors used in the study include elevation, slope degree, slope aspect, rainfall, temperature, lithology, land use, normalized difference vegetation index (NDVI), wind exposition index (WEI), topographic wetness index (TWI), plan curvature, drainage density, distance from roads, distance from rivers, and distance from villages. The spatial earthquake hazard in the study area was derived from a peak ground acceleration (PGA) susceptibility map. The second step in the study was to combine the model-generated maps of the four hazards in a reliable multi-hazard map. The mean decrease Gini (MDG) method was used to determine the level of importance of each effective factor on the occurrence of landslides, oods, and forest res. Finally, "area under the curve" (AUC) values were calculated to validate the forest re, ood, and landslide susceptibility maps and to compare the predictive capability of the machine learning models. The RF model yielded the highest AUC values for the forest re, ood, and landslide susceptibility maps, speci cally, 0.81, 0.85, and 0.94, respectively. and have been used in previous multi-hazard studies. Furthermore, a major contribution of this study is a concept multi-hazard map. Study AreaKhuzestan Province is located in southwest Iran. With an area of ~ 64,000 km 2 , it accounts for 4% of the country's total area. About 40% of the province is mountainous, with mild summers and cold winters. Foothill areas are semi-arid, whereas lowlands on the south and southwest are arid (Azimi et al., 2017).The winters in lowland areas are short and temperate and the summers are long and hot. Average annual rainfall in the province is 318 mm; lowland temperatures can reach 50°C in summer (Masoudi and Elhaeesahar, 2016). Methods Data collection and preparationBased on the experiences of the authors and expert opinions, four famous, well documented natural disasters in Khuzestan Province were selected for this study, one of each type ( ooding, landslide, wild re, and earthquake). The inventory map of each hazard was prepared. Data from geological survey and mineral explorations of South Bakhtari region (Ahvaz) organizations and ooded regions identi ed on Sentinel-1 satellite imagery in the Google Earth Engine environment were used to map the area affected by the ood event. Also, existing and historical data were used to prepare the landslides inventor...
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