A particle-based optimization of artificial neural network for earthquake-induced landslide assessment in Ludian county, China

• Published in: Geomatics, natural hazards and risk natural hazards and risk, 2019-01, Vol.10 (1), p.1750-1771
• Main Authors: Xi, Wenfei, Li, Guozhu, Moayedi, Hossein, Nguyen, Hoang
• Format: Article
• Language: English
• Subjects: Acceleration; Algorithms; Artificial neural network; Artificial neural networks; Distance; earthquake; Earthquakes; Elevation; Geographic information systems; Geographical information systems; GIS; hybrid algorithm; Hydrology; Information systems; landslide assessment; Landslides; Lithology; Ludian county; Multilayer perceptrons; Neural networks; Normalized difference vegetative index; Optimization techniques; Particle swarm optimization; Prediction models; Remote sensing; Rivers; Seismic activity; Seismic response; Slopes; Training; Vegetation index; Wetness index
• ISSN: 1947-5705; 1947-5713
• DOI: 10.1080/19475705.2019.1615005

The focal point of this study is to assess the efficacy of a state-of-the-art optimization technique namely, particle swarm optimization (PSO) for enhancing the performance of the artificial neural network (ANN) in modeling the seismic landslides at Ludian districts, China. Twelve geological and hydrological landslide-conditioning factors namely, elevation, lithology, slope degree, slope aspect, stream power index, peak ground acceleration, topographic wetness index, distance to river, distance to road, distance to fault, normalized difference vegetation index and plan curvature were considered within a geographic information system (GIS). After achieving the optimal structure of the multilayer perceptron neural network, the PSO algorithm was applied to improve its efficiency. The landslide susceptibility maps were generated in a GIS environment and area under the curve (AUC) criterion was used to assess the integrity of employed predictive models. The results showed that after applying the PSO algorithm, AUC experiences a significant increase from 0.765 to 0.825 in the validation phase. Moreover, respective AUCs of 0.812 and 0.828 obtained for the training phase of ANN and PSO-ANN reveal the efficiency of the proposed algorithm in improving the ANN accuracy.