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Water 2018, 10(9), 1210; https://doi.org/10.3390/w10091210

New Hybrids of ANFIS with Several Optimization Algorithms for Flood Susceptibility Modeling

1
Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam
2
Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam
3
Department of Watershed Management Engineering, Faculty of Natural Resources, Sari Agricultural Science and Natural Resources University, Sari 48181-68984, Iran
4
State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, China
5
Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, Iran
6
Young Researchers and Elites Club, North Tehran Branch, Islamic Azad University, Tehran 19585-466, Iran
7
Department of Biological and Agricultural Engineering & Zachry Department of Civil Engineering, Texas A & M University, College Station, TX 77843-2117, USA
8
Department of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, Iran
9
College of Geology & Environment, Xi’an University of Science and Technology, Xi’an 710054, China
10
Department of Geoinformation, Faculty of Geoinformation and Real Estate, Universiti Teknologi Malaysia, Skudai 81310, Malaysia
*
Authors to whom correspondence should be addressed.
Received: 6 August 2018 / Revised: 19 August 2018 / Accepted: 27 August 2018 / Published: 7 September 2018
(This article belongs to the Special Issue Flood Forecasting Using Machine Learning Methods)
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Abstract

This study presents three new hybrid artificial intelligence optimization models—namely, adaptive neuro-fuzzy inference system (ANFIS) with cultural (ANFIS-CA), bees (ANFIS-BA), and invasive weed optimization (ANFIS-IWO) algorithms—for flood susceptibility mapping (FSM) in the Haraz watershed, Iran. Ten continuous and categorical flood conditioning factors were chosen based on the 201 flood locations, including topographic wetness index (TWI), river density, stream power index (SPI), curvature, distance from river, lithology, elevation, ground slope, land use, and rainfall. The step-wise weight assessment ratio analysis (SWARA) model was adopted for the assessment of relationship between flood locations and conditioning factors. The ANFIS model, based on SWARA weights, was employed for providing FSMs with three optimization models to enhance the accuracy of prediction. To evaluate the model performance and prediction capability, root-mean-square error (RMSE) and receiver operating characteristic (ROC) curve (area under the ROC (AUROC)) were used. Results showed that ANFIS-IWO with lower RMSE (0.359) had a better performance, while ANFIS-BA with higher AUROC (94.4%) showed a better prediction capability, followed by ANFIS0-IWO (0.939) and ANFIS-CA (0.921). These models can be suggested for FSM in similar climatic and physiographic areas for developing measures to mitigate flood damages and to sustainably manage floodplains. View Full-Text
Keywords: flood susceptibility modeling; ANFIS; cultural algorithm; bees algorithm; invasive weed optimization; Haraz watershed flood susceptibility modeling; ANFIS; cultural algorithm; bees algorithm; invasive weed optimization; Haraz watershed
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Tien Bui, D.; Khosravi, K.; Li, S.; Shahabi, H.; Panahi, M.; Singh, V.P.; Chapi, K.; Shirzadi, A.; Panahi, S.; Chen, W.; Bin Ahmad, B. New Hybrids of ANFIS with Several Optimization Algorithms for Flood Susceptibility Modeling. Water 2018, 10, 1210.

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