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Gully erosion susceptibility mapping using GIS-based multi-criteria decision analysis techniques
- Arabameri, Alireza, Pradhan, Biswajeet, Rezaei, Khalil, Conoscenti, Christian
- Catena 2019 v.180 pp. 282-297
- geography, gully erosion, hydrology, information systems, inventories, kriging, models, multi-criteria decision making, prediction, semiarid zones, soil properties, topography, Iran
- This research introduces a scientific methodology for gully erosion susceptibility mapping (GESM) that employs geography information system (GIS)-based multi-criteria decision analysis. The model was tested in Semnan Province, Iran, which has an arid and semi-arid climate with high susceptibility to gully erosion. The technique for order of preference by similarity to ideal solution (TOPSIS) and the analytic hierarchy process (AHP) multi-criteria decision-making (MCDM) models were integrated. The important aspect of this research is that it did not require gully erosion inventory maps for GESM. Therefore, the proposed methodology could be useful in areas with missing or incomplete data. Fifteen variables reflecting topographic, hydrologic, geologic, environmental and soil characteristics were selected as proxies for gully erosion conditioning factors (GECFs). The experiment was conducted using 200 sample points that were selected randomly in the study area, and the weights of criteria (GECFs) were obtained using the AHP model. In the next step, the TOPSIS model was applied, and the weight of each alternative (sample points) was obtained. Kriging and inverse distance-weighted (IDW) methods were used for interpolation and GESM. Natural break method was used for classifying gully erosion susceptibility into five classes, from very low to very high. The area under the ROC curve (AUC) was used for validation. AHP results showed that distance to stream (0.14), slope degree (0.13) and distance to road (0.12) played major roles in controlling gully erosion in the study area. The values of points obtained by using the TOPSIS model ranged from 0.321 to 0.808. Verification results showed that kriging had higher prediction accuracy than IDW. The GESM results obtained by this methodology can be used by decision makers and managers to plan preventive measures and reduce damages due to gully erosion.