Flood susceptibility mapping in Dingnan County (China) using adaptive neuro-fuzzy inference system with biogeography based optimization and imperialistic competitive algorithm.
Flooding is one of the most significant environmental challenges and can easily cause fatal incidents and economic losses. Flood reduction is costly and time-consuming task; so it is necessary to accurately detect flood susceptible areas. This work presents an effective flood susceptibility mapping...
| Publicado en: | Journal of Environmental Management Vol. 247; pp. 712 - 730 |
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| Autores principales: | , , , , , , , , , |
| Formato: | Artículo |
| Publicado: |
Academic Press Inc.
Oct2019
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=138099337&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 138099337 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 03014797 EMJ jtl: Journal of Environmental Management issn: 03014797 maglogo: N pubinfo: dt: Oct2019 vid: 247 pid: 735 pub: Academic Press Inc. artinfo: ui: 138099337 10.1016/j.jenvman.2019.06.102 ppf: 712 ppct: 18 formats: tig: atl: Flood susceptibility mapping in Dingnan County (China) using adaptive neuro-fuzzy inference system with biogeography based optimization and imperialistic competitive algorithm. aug: au: Wang, Yi Hong, Haoyuan Chen, Wei Li, Shaojun Panahi, Mahdi Khosravi, Khabat Shirzadi, Ataollah Shahabi, Himan Panahi, Somayeh Costache, Romulus affil: Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan, 430074, China Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Nanjing, 210023, China State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing, 210023, China Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application, Nanjing, Jiangsu, 210023, China College of Geology and Environment, Xi'an University of Science and Technology, Xi'an, 710054, Shaanxi, China State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan, 430071, Hubei, China Young Researchers and Elites Club, North Tehran Branch, Islamic Azad University, Tehran, Iran Department of Watershed Management Engineering, Faculty of Natural Resources, Sari Agricultural Science and Natural Resources University (SANRU), Sari, Iran Department of Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran Research Institute of the University of Bucharest, 36-46 Bd. M. Kogalniceanu, 5th District, 050107, Bucharest, Romania National Institute of Hydrology and Water Management, București-Ploiești Road, 97E, 1st District, 013686, Bucharest, Romania su: China Landslide hazard analysis Sediment transport Biogeography Receiver operating characteristic curves Flood damage Floods Statistical errors Landslides sug: subj: China Specialized Freight (except Used Goods) Trucking, Local Specialized Freight (except Used Goods) Trucking, Long-Distance Landslide hazard analysis Sediment transport Biogeography Receiver operating characteristic curves Flood damage Floods Statistical errors Landslides keyword: Adaptive neuro-fuzzy inference system Biogeography based optimization Flood susceptibility mapping Imperialistic competitive algorithm Metaheuristic methods Adaptive neuro-fuzzy inference system Biogeography based optimization Flood susceptibility mapping Imperialistic competitive algorithm Metaheuristic methods ab: Flooding is one of the most significant environmental challenges and can easily cause fatal incidents and economic losses. Flood reduction is costly and time-consuming task; so it is necessary to accurately detect flood susceptible areas. This work presents an effective flood susceptibility mapping framework by involving an adaptive neuro-fuzzy inference system (ANFIS) with two metaheuristic methods of biogeography based optimization (BBO) and imperialistic competitive algorithm (ICA). A total of 13 flood influencing factors, including slope, altitude, aspect, curvature, topographic wetness index, stream power index, sediment transport index, distance to river, landuse, normalized difference vegetation index, lithology, rainfall and soil type, were used in the proposed framework for spatial modeling and Dingnan County in China was selected for the application of the proposed methods due to data availability. There are 115 flood occurrences in the study area which were randomly separated into training (70% of the total) and verification (30%) sets. To perform the proposed framework, the step-wise weight assessment ratio analysis algorithm is first used to evaluate the correlation between influencing factors and floods. Then, two ensemble methods of ANFIS-BBO and ANFIS-ICA are constructed for spatial prediction and producing flood susceptibility maps. Finally, these resultant maps are assessed in terms of several statistical and error measures, including receiver operating characteristic (ROC) curve and area under the ROC curve (AUC), root-mean-square error (RMSE). The experimental results demonstrated that the two ensemble methods were more effective than ANFIS in the study area. For instance, the predictive AUC values of 0.8407, 0.9045 and 0.9044 were achieved by the methods of ANFIS, ANFIS-BBO and ANFIS-ICA, respectively. Moreover, the RMSE values for ANFIS, ANFIS-BBO and ANFIS-ICA using the verification set were 0.3100, 0.2730 and 0.2700, respectively. In addition, as regards ANFIS-BBO and ANFIS-ICA, a total areas of 39.30% and 35.39% were classified as highly susceptible to flooding. Therefore, the proposed ensemble framework can be used for flood susceptibility mapping in other sites with similar geo-environmental characteristics for taking measures to manage and prevent flood damages. Image 1 • Prediction power of two novel ensemble methods for flood susceptibility mapping. • The proposed ensemble methods can improve the prediction performance of ANFIS. • The proposed methods can accurately produce flood susceptibility maps for mitigation and management. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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