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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Published in | Geomatics, natural hazards and risk Vol. 10; no. 1; pp. 1750 - 1771 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
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01.01.2019
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Abstract | 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. |
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AbstractList | 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. |
Author | Li, Guozhu Nguyen, Hoang Xi, Wenfei Moayedi, Hossein |
Author_xml | – sequence: 1 givenname: Wenfei orcidid: 0000-0002-8576-7096 surname: Xi fullname: Xi, Wenfei organization: , Yunnan Normal University – sequence: 2 givenname: Guozhu orcidid: 0000-0002-7849-9951 surname: Li fullname: Li, Guozhu organization: Yunnan Haiju Geographic Information Technology Co, Ltd – sequence: 3 givenname: Hossein orcidid: 0000-0002-5625-1437 surname: Moayedi fullname: Moayedi, Hossein email: hossein.moayedi@tdtu.edu.vn, wenfeixi@ynnu.edu.cn organization: Faculty of Civil Engineering, Ton Duc Thang University – sequence: 4 givenname: Hoang orcidid: 0000-0001-6122-8314 surname: Nguyen fullname: Nguyen, Hoang organization: Institute of Research and Development, Duy Tan University |
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SubjectTerms | 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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Title | A particle-based optimization of artificial neural network for earthquake-induced landslide assessment in Ludian county, China |
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