Landslide susceptibility mapping using Genetic Algorithm for the Rule Set Production (GARP) model Landslide susceptibility mapping using Genetic Algorithm for the Rule Set Production (GARP) model

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Vol15 No.9: 2013-2026

Title】Landslide susceptibility mapping using Genetic Algorithm for the Rule Set Production (GARP) model

Author】Fatemeh ADINEH; Baharak MOTAMEDVAZIRI*; Hasan AHMDI; Abolfazl MOEINI

Addresses】Department of Forest, Range and Watershed Management, Science and Research Branch, Islamic Azad University, Tehran, Iran

Corresponding author】Baharak MOTAMEDVAZIRI

Citation】Adineh F,Motamedvaziri B, Ahmdi H, et al. (2018) Landslide susceptibility mapping using Genetic Algorithm for the Rule Set Production (GARP) model. Journal of Mountain Science 15(9). https://doi.org/10.1007/s11629-018-4833-5

DOI】https://doi.org/10.1007/s11629-018-4833-5

Abstract】Investigation on landslide phenomenon is necessary for understanding and delineating the landslide prone and safer places for different land use practices. On this basis, a new model known as genetic algorithm for the rule set production was applied in order to assess its efficacy to obtain a better result and a more precise landslide susceptibility map in Klijanerestagh area of Iran. This study considered twelve landslide conditioning factors (LCF) like altitude, slope, aspect, plan curvature, profile curvature, topographic wetness index (TWI), distance from rivers, faults, and roads, land use/cover, and lithology. For modeling purpose, the Genetic Algorithm for the Rule Set Production (GARP) algorithm was applied in order to produce the landslide susceptibility map. Finally, to evaluate the efficacy of the GARP model, receiver operating characteristics curve as well as the Kappa index were employed. Based on these indices, the GARP model predicted the probability of future landslide incidences with the area under the receiver operating characteristics curve (AUC-ROC) values of 0.932, and 0.907 for training and validating datasets, respectively. In addition, Kappa values for the training and validating datasets were computed as 0.775, and 0.716, respectively. Thus, it can be concluded that the GARP algorithm can be a new but effective method for generating landslide susceptibility maps (LSMs). Furthermore, higher contribution of the lithology, distance from roads, and distance from faults was observed, while lower contribution was attributed to soil, profile curvature, and TWI factors. The introduced methodology in this paper can be suggested for other areas with similar topographical and hydrogeological characteristics for land use planning and reducing the landslide damages.

Keywords】Landslide susceptibility mapping; GIS; GARP model; Klijanerestagh watershed; Iran