Hybrids of Support Vector Regression with Grey Wolf Optimizer and Firefly Algorithm for Spatial Prediction of Landslide Susceptibility

Landslides are one of the most frequent and important natural disasters in the world. The purpose of this study is to evaluate the landslide susceptibility in Zhenping County using a hybrid of support vector regression (SVR) with grey wolf optimizer (GWO) and firefly algorithm (FA) by frequency rati...

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Veröffentlicht in:	Remote sensing (Basel, Switzerland) Jg. 13; H. 24; S. 4966
Hauptverfasser:	Liu, Ru, Peng, Jianbing, Leng, Yanqiu, Lee, Saro, Panahi, Mahdi, Chen, Wei, Zhao, Xia
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.12.2021
Schlagworte:	Algorithms Artificial intelligence Conditioning Correlation analysis Curvature data collection Datasets Decision making Disasters Elevation Fault lines firefly algorithm Geographic information systems Geology grey wolf optimizer algorithm Heuristic methods Human engineering hybrid model Hybrids inventories Land use planning landslide susceptibility Landslides Landslides & mudslides Lithology Machine learning Mapping Natural disasters Neural networks Normalized difference vegetative index Performance enhancement prediction Predictions rain Rainfall regression analysis Remote sensing Sediment transport Software soil Soil conditions Spatial analysis streams Support vector machines support vector regression algorithm Topography Training Vegetation
ISSN:	2072-4292, 2072-4292
Online-Zugang:	Volltext
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