Staff View: Spatial prediction of landslide susceptibility using GIS-based data mining techniques of ANFIS with Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO)

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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Main Authors:	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, Dieu, Tien Bui, Ahmad, Baharin
Format:	Article
Language:	English
Published:	MDPI AG 2019
Subjects:	G70.212-70.215 Geographic information system
Online Access:	http://eprints.utm.my/id/eprint/87333/1/BaharinAhmad2019_SpatialPredictionofLandslideSusceptibilityUsingGIS-Based.pdf http://eprints.utm.my/id/eprint/87333/ http://dx.doi.org/10.3390/app9183755
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spelling	my.utm.873332020-11-08T03:55:20Z http://eprints.utm.my/id/eprint/87333/ Spatial prediction of landslide susceptibility using GIS-based data mining techniques of ANFIS with Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO) 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 Dieu, Tien Bui Ahmad, Baharin G70.212-70.215 Geographic information system 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. MDPI AG 2019-09 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/87333/1/BaharinAhmad2019_SpatialPredictionofLandslideSusceptibilityUsingGIS-Based.pdf Chen, Wei and Hong, Haoyuan and Panahi, Mahdi and Shahabi, Himan and Wang, Yi and Shirzadi, Ataollah and Pirasteh, Saied and Alesheikh, Ali Asghar and Khosravi, Khabat and Panahi, Somayeh and Rezaie, Fatemeh and Li, Shaojun and Jaafari, Abolfazl and Dieu, Tien Bui and Ahmad, Baharin (2019) Spatial prediction of landslide susceptibility using GIS-based data mining techniques of ANFIS with Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO). Applied Sciences (Switzerland), 9 (18). p. 3755. ISSN 2076-3417 http://dx.doi.org/10.3390/app9183755
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
language	English
topic	G70.212-70.215 Geographic information system
spellingShingle	G70.212-70.215 Geographic information system 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 Dieu, Tien Bui Ahmad, Baharin Spatial prediction of landslide susceptibility using GIS-based data mining techniques of ANFIS with Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO)
description	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.
format	Article
author	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 Dieu, Tien Bui Ahmad, Baharin
author_facet	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 Dieu, Tien Bui Ahmad, Baharin
author_sort	Chen, Wei
title	Spatial prediction of landslide susceptibility using GIS-based data mining techniques of ANFIS with Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO)
title_short	Spatial prediction of landslide susceptibility using GIS-based data mining techniques of ANFIS with Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO)
title_full	Spatial prediction of landslide susceptibility using GIS-based data mining techniques of ANFIS with Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO)
title_fullStr	Spatial prediction of landslide susceptibility using GIS-based data mining techniques of ANFIS with Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO)
title_full_unstemmed	Spatial prediction of landslide susceptibility using GIS-based data mining techniques of ANFIS with Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO)
title_sort	spatial prediction of landslide susceptibility using gis-based data mining techniques of anfis with whale optimization algorithm (woa) and grey wolf optimizer (gwo)
publisher	MDPI AG
publishDate	2019
url	http://eprints.utm.my/id/eprint/87333/1/BaharinAhmad2019_SpatialPredictionofLandslideSusceptibilityUsingGIS-Based.pdf http://eprints.utm.my/id/eprint/87333/ http://dx.doi.org/10.3390/app9183755
_version_	1683230756055285760
score	13.211869

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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