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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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College of Geology and Environment, Xi’an University of Science and Technology, Xi’an 710054, China
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Key Laboratory of Mine Geological Hazard Mechanism and Control, Shaanxi Institute of Geo-Environment Monitoring, Xi’an 710054, China
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Key Laboratory of Coal Resources Exploration and Comprehensive Utilization, Ministry of Land and Resources, Xi’an 710021, China
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Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Nanjing 210023, China
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State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing 210023, China
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Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application, Nanjing 210023, China
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Geoscience Platform Research Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124, Gwahak-ro Yuseong-gu, Daejeon 34132, Korea
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Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, Iran
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Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China
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Department of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, Iran
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Department of Surveying and Geoinformatics, Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Xipu Campus, Chengdu 611756, China
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Department of GIS, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran 19967-15433, Iran
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School of Engineering, University of Guelph, Guelph, ON N1G 2W1, Canada
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Young Researchers and Elites Club, North Tehran Branch, Islamic Azad University, Tehran 19585-466, Iran
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State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, China
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Research Institute of Forests and Rangelands, Agricultural Research, Education, and Extension Organization (AREEO), Tehran 13185-116, Iran
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Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
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Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia (UTM), Johor Bahru 81310, Malaysia
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Authors to whom correspondence should be addressed.
Appl. Sci. 2019, 9(18), 3755; https://doi.org/10.3390/app9183755
Received: 14 August 2019 / Revised: 31 August 2019 / Accepted: 3 September 2019 / Published: 8 September 2019
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. View Full-Text
Keywords: landslide; evolutionary optimization algorithm; prediction accuracy; goodness-of-fit; machine learning; China landslide; evolutionary optimization algorithm; prediction accuracy; goodness-of-fit; machine learning; China
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MDPI and ACS Style

Chen, W.; Hong, H.; Panahi, M.; Shahabi, H.; Wang, Y.; Shirzadi, A.; Pirasteh, S.; Alesheikh, A.A.; Khosravi, K.; Panahi, S.; Rezaie, F.; Li, S.; Jaafari, A.; Bui, D.T.; Bin Ahmad, B. Spatial Prediction of Landslide Susceptibility Using GIS-Based Data Mining Techniques of ANFIS with Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO). Appl. Sci. 2019, 9, 3755. https://doi.org/10.3390/app9183755

AMA Style

Chen W, Hong H, Panahi M, Shahabi H, Wang Y, Shirzadi A, Pirasteh S, Alesheikh AA, Khosravi K, Panahi S, Rezaie F, Li S, Jaafari A, Bui DT, Bin Ahmad B. 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. 2019; 9(18):3755. https://doi.org/10.3390/app9183755

Chicago/Turabian Style

Chen, Wei, Haoyuan Hong, Mahdi Panahi, Himan Shahabi, Yi Wang, Ataollah Shirzadi, Saied Pirasteh, Ali A. Alesheikh, Khabat Khosravi, Somayeh Panahi, Fatemeh Rezaie, Shaojun Li, Abolfazl Jaafari, Dieu T. Bui, and Baharin Bin Ahmad. 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 9, no. 18: 3755. https://doi.org/10.3390/app9183755

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