A GIS-based multi-objective evolutionary algorithm for landslide susceptibility mapping

Seyed Vahid Razavi-Termeh

doi:10.1007/s00477-023-02562-6

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A GIS-based multi-objective evolutionary algorithm for landslide susceptibility mapping

2023-10-30

Seyed Vahid Razavi-Termeh

Javad Hatamiafkoueieh

Abolghasem Sadeghi‐Niaraki *

Soo-Mi Choi

Khalifa M. Al‐Kindi

DOI：10.1007/s00477-023-02562-6

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Abstract

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Landslides pose a significant threat to human life and infrastructure, underscoring the ongoing need for accurate landslide susceptibility mapping (LSM) to effectively assess risks. This study introduces an innovative approach that leverages multi-objective evolutionary fuzzy algorithms for landslide modeling in Khalkhal town, Iran. Two algorithms, namely the non-dominated sorting genetic algorithm II (NSGA-II) and the evolutionary non-dominated radial slots-based algorithm (ENORA), were employed to optimize Gaussian fuzzy rules. By utilizing 15 landslide conditioning factors (aspect, altitude, distance from the fault, soil, slope, lithology, rainfall, distance from the road, the normalized difference vegetation index (NDVI), land cover, plan curvature, profile curvature, topographic wetness index (TWI), stream power index (SPI), and distance from the river) and historical landslide events (153 landslide locations), we randomly partitioned the input data into training (70%) and validation (30%) sets. The training set determined the weight of conditioning factor classes using the frequency ratio (FR) approach. These weights were then used as inputs for the NSGA-II and ENORA algorithms to generate an LSM. The NSGA-II algorithm achieved a root-mean-square error (RMSE) of 0.25 during training and 0.43 during validation. Similarly, the ENORA algorithm demonstrated an RMSE of 0.28 in training and 0.48 in validation. The findings revealed that the LSM created by the NSGA-II algorithm exhibited superior predictive capabilities (area under the receiver operating characteristic curve (AUC) = 0.867) compared to the ENORA algorithm (AUC = 0.844). Additionally, a particle swarm optimization (PSO) algorithm was employed to determine the importance of conditioning factors, identifying lithology, land cover, and altitude as the most influential factors.

Keywords:

Landslide

Spatial prediction

Multi-objective evolutionary fuzzy algorithm

Remote sensing