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A Comparative Study of Kernel Logistic Regression, Radial Basis Function Classifier, Multinomial Naïve Bayes, and Logistic Model Tree for Flash Flood Susceptibility Mapping

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University of Transport Technology, Hanoi 100000, Viet Nam
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Institute of Geological Sciences, Vietnam Academy of Sciences and Technology, 84 Chua Lang Street, Dong da, Hanoi 100000, Viet Nam
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Faculty of Geography, VNU University of Science, 334 Nguyen Trai, Hanoi 100000, Viet Nam
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School of Resources and Safety Engineering, Central South University, Changsha 410083, China
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Department of Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, 971 87 Lulea, Sweden
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Kurdistan Agricultural and Natural Resources Research and Education Center, AREEO, Sanandaj 66177-15175, Iran
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Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
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Department of Resource and Environment Management, School of Agriculture and Resources, Vinh University, Nghe An 470000, Vietnam
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Department of Geography, School of Social Education, Vinh University, Nghe An 470000, Vietnam
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Department of Science & Technology, Bhaskarcharya Institute for Space Applications and Geo-Informatics (BISAG), Government of Gujarat, Gandhinagar 382002, India
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Geographic Information System group, Department of Business and IT, University of South-Eastern Norway, 3674 Notodden, Norway
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Authors to whom correspondence should be addressed.
Water 2020, 12(1), 239; https://doi.org/10.3390/w12010239 (registering DOI)
Received: 30 September 2019 / Revised: 7 January 2020 / Accepted: 10 January 2020 / Published: 15 January 2020
(This article belongs to the Special Issue Advances in Flash Flood Forecasting)
Risk of flash floods is currently an important problem in many parts of Vietnam. In this study, we used four machine-learning methods, namely Kernel Logistic Regression (KLR), Radial Basis Function Classifier (RBFC), Multinomial Naïve Bayes (NBM), and Logistic Model Tree (LMT) to generate flash flood susceptibility maps at the minor part of Nghe An province of the Center region (Vietnam) where recurrent flood problems are being experienced. Performance of these four methods was evaluated to select the best method for flash flood susceptibility mapping. In the model studies, ten flash flood conditioning factors, namely soil, slope, curvature, river density, flow direction, distance from rivers, elevation, aspect, land use, and geology, were chosen based on topography and geo-environmental conditions of the site. For the validation of models, the area under Receiver Operating Characteristic (ROC), Area Under Curve (AUC), and various statistical indices were used. The results indicated that performance of all the models is good for generating flash flood susceptibility maps (AUC = 0.983–0.988). However, performance of LMT model is the best among the four methods (LMT: AUC = 0.988; KLR: AUC = 0.985; RBFC: AUC = 0.984; and NBM: AUC = 0.983). The present study would be useful for the construction of accurate flash flood susceptibility maps with the objectives of identifying flood-susceptible areas/zones for proper flash flood risk management. View Full-Text
Keywords: flash flood; kernel logistic regression; radial basis function network; multinomial naïve Bayes; logistic model tree; machine learning; Vietnam flash flood; kernel logistic regression; radial basis function network; multinomial naïve Bayes; logistic model tree; machine learning; Vietnam
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Pham, B.T.; Phong, T.V.; Nguyen, H.D.; Qi, C.; Al-Ansari, N.; Amini, A.; Ho, L.S.; Tuyen, T.T.; Yen, H.P.H.; Ly, H.-B.; Prakash, I.; Tien Bui, D. A Comparative Study of Kernel Logistic Regression, Radial Basis Function Classifier, Multinomial Naïve Bayes, and Logistic Model Tree for Flash Flood Susceptibility Mapping. Water 2020, 12, 239.

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