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New hybrids of ANFIS with several optimization algorithms for flood susceptibility modeling

Dieu, Tien Bui and Khosravi, Khabat and Li, Shaojun and Shahabi, Himan and Panahi, Mahdi and Singh, Vijay P. and Chapi, Kamran and Shirzadi, Ataollah and Panahi, Somayeh and Chen, Wei and Ahmad, Baharin (2018) New hybrids of ANFIS with several optimization algorithms for flood susceptibility modeling. Water (Switzerland), 10 (9). ISSN 2073-4441

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Official URL: http://dx.doi.org/10.3390/w10091210

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.

Item Type:Article
Uncontrolled Keywords:ANFIS, bees algorithm, cultural algorithm, flood susceptibility modeling, haraz watershed, invasive weed optimization
Subjects:G Geography. Anthropology. Recreation > G Geography (General)
Divisions:Geoinformation and Real Estate
ID Code:86272
Deposited By: Yanti Mohd Shah
Deposited On:31 Aug 2020 21:54
Last Modified:31 Aug 2020 21:54

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