Spatial Prediction of Landslide Susceptibility Using GIS-Based Data Mining Techniques of ANFIS with Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO)

The most dangerous landslide disasters always cause serious economic losses and human deaths. The contribution of this work is to present an integrated landslide modelling framework, in which an adaptive neuro-fuzzy inference system (ANFIS) is combined with the two optimization algorithms of whale o...

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Vydané v:Applied sciences Ročník 9; číslo 18; s. 3755
Hlavní autori: Chen, Wei, Hong, Haoyuan, Panahi, Mahdi, Shahabi, Himan, Wang, Yi, Shirzadi, Ataollah, Pirasteh, Saied, Alesheikh, Ali Asghar, Khosravi, Khabat, Panahi, Somayeh, Rezaie, Fatemeh, Li, Shaojun, Jaafari, Abolfazl, Bui, Dieu Tien, Bin Ahmad, Baharin
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.09.2019
Predmet:
Accuracy
Altitude
China
evolutionary optimization algorithm
Fuzzy logic
GDP
Gene expression
goodness-of-fit
Gross Domestic Product
Land use
landslide
Landslides & mudslides
Lithology
machine learning
Neural networks
Optimization algorithms
prediction accuracy
Remote sensing
Rivers
Support vector machines
United States > US
China
ISSN:2076-3417, 2076-3417
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Abstract The most dangerous landslide disasters always cause serious economic losses and human deaths. The contribution of this work is to present an integrated landslide modelling framework, in which an adaptive neuro-fuzzy inference system (ANFIS) is combined with the two optimization algorithms of whale optimization algorithm (WOA) and grey wolf optimizer (GWO) at Anyuan County, China. It means that WOA and GWO are used as two meta-heuristic algorithms to improve the prediction performance of the ANFIS-based methods. In addition, the step-wise weight assessment ratio analysis (SWARA) method is used to obtain the initial weight of each class of landslide influencing factors. To validate the effectiveness of the proposed framework, 315 landslide events in history were selected for our experiments and were randomly divided into the training and verification sets. To perform landslide susceptibility mapping, fifteen geological, hydrological, geomorphological, land cover, and other factors are considered for the modelling construction. The landslide susceptibility maps by SWARA, SWARA-ANFIS, SWARA-ANFIS-PSO, SWARA-ANFIS-WOA, and SWARA-ANFIS-GWO models are assessed using the measures of the receiver operating characteristic (ROC) curve and root-mean-square error (RMSE). The experiments demonstrated that the obtained results of modelling process from the SWARA to the SAWRA-ANFIS-GWO model were more accurate and that the proposed methods have satisfactory prediction ability. Specifically, prediction accuracy by area under the curve (AUC) of SWARA, SWARA-ANFIS, SWARA-ANFIS-PSO, SWARA-ANFIS-GWO, and SWARA-ANFIS-WOA models were 0.831, 0.831, 0.850, 0.856, and 0.869, respectively. Due to adaptability and usability, the proposed prediction methods can be applied to other areas for landslide management and mitigation as well as prevention throughout the world.
AbstractList The most dangerous landslide disasters always cause serious economic losses and human deaths. The contribution of this work is to present an integrated landslide modelling framework, in which an adaptive neuro-fuzzy inference system (ANFIS) is combined with the two optimization algorithms of whale optimization algorithm (WOA) and grey wolf optimizer (GWO) at Anyuan County, China. It means that WOA and GWO are used as two meta-heuristic algorithms to improve the prediction performance of the ANFIS-based methods. In addition, the step-wise weight assessment ratio analysis (SWARA) method is used to obtain the initial weight of each class of landslide influencing factors. To validate the effectiveness of the proposed framework, 315 landslide events in history were selected for our experiments and were randomly divided into the training and verification sets. To perform landslide susceptibility mapping, fifteen geological, hydrological, geomorphological, land cover, and other factors are considered for the modelling construction. The landslide susceptibility maps by SWARA, SWARA-ANFIS, SWARA-ANFIS-PSO, SWARA-ANFIS-WOA, and SWARA-ANFIS-GWO models are assessed using the measures of the receiver operating characteristic (ROC) curve and root-mean-square error (RMSE). The experiments demonstrated that the obtained results of modelling process from the SWARA to the SAWRA-ANFIS-GWO model were more accurate and that the proposed methods have satisfactory prediction ability. Specifically, prediction accuracy by area under the curve (AUC) of SWARA, SWARA-ANFIS, SWARA-ANFIS-PSO, SWARA-ANFIS-GWO, and SWARA-ANFIS-WOA models were 0.831, 0.831, 0.850, 0.856, and 0.869, respectively. Due to adaptability and usability, the proposed prediction methods can be applied to other areas for landslide management and mitigation as well as prevention throughout the world.
Author Pirasteh, Saied
Bin Ahmad, Baharin
Shirzadi, Ataollah
Alesheikh, Ali Asghar
Bui, Dieu Tien
Shahabi, Himan
Jaafari, Abolfazl
Chen, Wei
Wang, Yi
Panahi, Somayeh
Panahi, Mahdi
Khosravi, Khabat
Li, Shaojun
Hong, Haoyuan
Rezaie, Fatemeh
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Snippet The most dangerous landslide disasters always cause serious economic losses and human deaths. The contribution of this work is to present an integrated...
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SubjectTerms Accuracy
Altitude
China
evolutionary optimization algorithm
Fuzzy logic
GDP
Gene expression
goodness-of-fit
Gross Domestic Product
Land use
landslide
Landslides & mudslides
Lithology
machine learning
Neural networks
Optimization algorithms
prediction accuracy
Remote sensing
Rivers
Support vector machines
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Title Spatial Prediction of Landslide Susceptibility Using GIS-Based Data Mining Techniques of ANFIS with Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO)
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