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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)
文献类型：期刊论文
| 作者 | Chen, Wei; Hong, Haoyuan; Panahi, Mandi; Shahabi, Himan; Wang, Yi; Shirzadi, Ataollah; Pirasteh, Saied; Alesheikh, Ali Asghar; Khosravi, Khabat; Panahi, Somayeh |
| 刊名 | APPLIED SCIENCES-BASEL
 |
| 出版日期 | 2019 |
| 卷号 | 9期号:18页码:- |
| 关键词 | landslide evolutionary optimization algorithm prediction accuracy goodness-of-fit machine learning China |
| DOI | 10.3390/app9183755 |
| 英文摘要 | 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. |
| WOS研究方向 | Chemistry ; Engineering ; Materials Science ; Physics |
| 语种 | 英语 |
| WOS记录号 | WOS:000489115200115 |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/14890]  |
| 专题 | 岩土力学所知识全产出_期刊论文 国家重点实验室知识产出_期刊论文 |
| 作者单位 | 1.Nanjing Normal Univ, Key Lab Virtual Geog Environm, Nanjing 210023, Jiangsu, Peoples R China 2.Xian Univ Sci & Technol, Coll Geol & Environm, Xian 710054, Shaanxi, Peoples R China; 3.Shaanxi Inst Geoenvironm Monitoring, Key Lab Mine Geol Hazard Mech & Control, Xian 710054, Shaanxi, Peoples R China; 4.Minist Land & Resources, Key Lab Coal Resources Explorat & Comprehens Util, Xian 710021, Shaanxi, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Chen, Wei,Hong, Haoyuan,Panahi, Mandi,et al. Spatial Prediction of Landslide Susceptibility Using GIS-Based Data Mining Techniques of ANFIS with Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO)[J]. APPLIED SCIENCES-BASEL,2019,9(18):-. |
| APA | Chen, Wei.,Hong, Haoyuan.,Panahi, Mandi.,Shahabi, Himan.,Wang, Yi.,...&Bin 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-BASEL,9(18),-. |
| MLA | Chen, Wei,et al."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-BASEL 9.18(2019):-. |
入库方式： OAI收割
来源：武汉岩土力学研究所
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