Potential of hybrid evolutionary approaches for assessment of geo-hazard landslide susceptibility mapping

Abstract

As a prevalent disaster, landslides cause severe loss of property and human life worldwide. The specific objective of this study is to evaluate the capability of artificial neural network (ANN) synthesized with artificial bee colony (ABC) and particle swarm optimization (PSO) evolutionary algorithms, in order to draw the landslide susceptibility map (LSM) at Golestan province, Iran. The required spatial database was created from 12 landslide conditioning factors. The area under curve (AUC) criterion was used to assess the integrity of employed predictive approaches. In this regard, the calculated AUCs of 90.10%, 85.70%, 80.30% and 76.60%, respectively, for SI, PSO-ANN, ABC-ANN and ANN showed that all models have enough accuracy for simulating the LSM, although SI presents the best performance. The landslide vulnerability map obtained by PSO-ANN model is more accurate than other intelligent techniques. In addition, training the ANN with ABC and PSO optimization algorithms conduced to enhancing the reliability of this model. Note that, a total of 76.72%, 23.96%, 30.55% and 5.37% of the study area were labeled as perilous (High and Very high susceptibility classes), respectively by SI, PSO-ANN, ABC-ANN and ANN results.