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

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 hyd...

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Bibliographic Details
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
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Summary: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.
ISSN:1947-5705
1947-5713
DOI:10.1080/19475705.2019.1615005