Comparative performance of new hybrid ANFIS models in landslide susceptibility mapping
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Comparative performance of new hybrid ANFIS models in landslide susceptibility mapping Sina Paryani1 · Aminreza Neshat1 · Saman Javadi2 · Biswajeet Pradhan3,4 Received: 10 February 2020 / Accepted: 13 May 2020 © Springer Nature B.V. 2020
Abstract Many landslides occur in the Karun watershed in the Zagros Mountains. In the present study, we employed a novel comparative approach for spatial modeling of landslides given the high potential of landslides in the region. The aim of the study was to combine adaptive neuro-fuzzy inference system (ANFIS) with grey wolf optimizer (GWO) and particle swarm optimizer (PSO) algorithms using the outputs of qualitative stepwise weight assessment ratio analysis (SWARA) and quantitative certainty factor (CF) models. To this end, 264 landslide positions and twelve conditioning factors including slope, aspect, altitude, distance to faults, distance to rivers, distance to roads, land use, lithology, rainfall, plan and profile curvature and TWI were then extracted considering regional characteristics, literature review and available data. In the next step, the multi-criteria SWARA decision-making model and CF probability model were used to evaluate a correlation between landslide distribution and conditioning factors. Ultimately, landslide susceptibility maps were generated by ANFIS-GWO and ANFIS-PSO hybrid models and the accuracy of models was assessed by ROC curve. According to the results, the area under the curve (AUC) for the hybrid models ANFIS - GWOSWARA , ANFIS - PSOSWARA , ANFIS - GWOCF and ANFIS - PSOCF was 0.789, 0.838, 0.850 and 0.879, respectively. The hybrid models ANFIS - PSOCF and ANFIS - GWOSWARA showed the highest and lowest prediction rate, respectively. Moreover, CF outperformed the SWARA method in terms of evaluating correlation between conditioning factors and landslides. The map produced in this study can be used by regional authorities to manage landslide risk.
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Vol.:(0123456789)
Natural Hazards
Graphic abstract
Keywords Landslide susceptibility · ANFIS · Grey wolf optimization · Particle swarm optimization · GIS
1 Introduction Landslides occur on steep slopes of hills and mountainous areas causing mortality, economic losses, damage to water and soil resources (Schlögel et al. 2015; Raja et al. 2017). This mass movement occurs whenever the loading of an earth material exceeds its shear strength (Lin and Lin 2017). Although landslides have always occurred over time, factors such as changes in climate patterns, constant deforestation of mountainous regions and increased urbanization and its development in susceptible areas have increased landslides around the world in recent years (Goetz et al. 2011). According to a report by the Centre for Research on Epidemiology of Natural Disasters (CRED), landslides are responsible for at least 17% of losses caused by natural disasters in the world (Chen et al. 2019a, b). According to Nadim et al. (2006), the South America, the northern part
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