Abstract
Four state-of-the-art metaheuristic algorithms including the genetic algorithm (GA), particle swarm optimization (PSO), differential evolutionary (DE), and ant colony optimization (ACO) are applied to an adaptive neuro-fuzzy inference system (ANFIS) for spatial prediction of landslide susceptibility in Qazvin Province (Iran). To this end, the landslide inventory map, composed of 199 identified landslides, is divided into training and testing landslides with a 70:30 ratio. To create the spatial database, thirteen landslide conditioning factors are considered within the geographic information system (GIS). Notably, the spatial interaction between the landslides and mentioned conditioning factors is analyzed by means of frequency ratio (FR) theory. After the optimization process, it was shown that the DE-based model reaches the best response more quickly than other ensembles. The landslide susceptibility maps were developed, and the accuracy of the models was evaluated by a ranking system, based on the calculated area under the receiving operating characteristic curve (AUROC), mean absolute error, and mean square error (MSE) accuracy indices. According to the results, the GA-ANFIS with a total ranking score (TRS) = 24 presented the most accurate prediction, followed by PSO-ANFIS (TRS = 17), DE-ANFIS (TRS = 13), and ACO-ANFIS (TRS = 6). Due to the excellent results of this research, the developed landslide susceptibility maps can be applied for future planning and decision making of the related area.
Highlights
Slope failures are ubiquitous major disasters causing many financial and physical damages worldwide every year
This paper investigates the efficiency of four evolutionary ensembles of a fuzzy-based model, namely genetic algorithm (GA)-adaptive neuro-fuzzy inference system (ANFIS), particle swarm optimization (PSO)-ANFIS, differential evolutionary (DE)-ANFIS, and ant colony optimization (ACO)-ANFIS for landslide susceptibility assessment
In this work, considering the common ratio of 70:30, 139 landslides were specified to the training phase, and the remaining 60 landslide points were used to measure the accuracy of the applied models
Summary
Slope failures are ubiquitous major disasters causing many financial and physical damages worldwide every year. Varnes and Radbruch-Hall [1] presented a definition of a landslide as any downward mass movement caused by gravity on slopes (e.g., artificial deposits, soil, and natural cliffs). Sensors 2020, 20, 1723 landslide events that have occurred around the world. It is noteworthy that the largest debris flow in the world, the Seimareh landslide, occurred in western Iran [3]. In another event, slope cutting and removal of the toe buttress triggered the Manjil landslide in 2013. Slope cutting and removal of the toe buttress triggered the Manjil landslide in 2013 It occurred on the Qazvin–Rasht freeway and led to blockage of the freeway [4]
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