sustainability Article Prediction Success of Machine Learning Methods for Flash Flood Susceptibility Mapping in the Tafresh Watershed, Iran Saeid Janizadeh 1, Mohammadtaghi Avand 1, Abolfazl Jaafari 2 , Tran Van Phong 3 , Mahmoud Bayat 2 , Ebrahim Ahmadisharaf 4 , Indra Prakash 5, Binh Thai Pham 6,* and Saro Lee 7,8,* 1 Department of Watershed Management Engineering, College of Natural Resources, Tarbiat Modares University, Tehran, P.O. Box 14115-111, Iran;
[email protected] (S.J.);
[email protected] (M.A.) 2 Research Institute of Forests and Rangelands, Agricultural Research, Education, and Extension Organization (AREEO), Tehran 13185-116, Iran;
[email protected] (A.J.);
[email protected] (M.B.) 3 Institute of Geological Sciences, Vietnam Academy of Sciences and Technology, 84 Chua Lang Street, Dong da, Hanoi, 100000, Viet Nam;
[email protected] 4 DHI, Lakewood, CO 80228, USA;
[email protected] 5 Department of Science & Technology, Bhaskarcharya Institute for Space Applications and Geo-Informatics (BISAG), Government of Gujarat, Gandhinagar 382007, India;
[email protected] 6 Institute of Research and Development, Duy Tan University, Da Nang 550000, Viet Nam 7 Geoscience Platform Research Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124, Gwahak-ro Yuseong-gu, Daejeon 34132, Korea 8 Department of Geophysical Exploration, Korea University of Science and Technology, 217 Gajeong-ro Yuseong-gu, Daejeon 34113, Korea * Correspondence:
[email protected] (B.T.P.);
[email protected] (S.L.) Received: 7 September 2019; Accepted: 29 September 2019; Published: 30 September 2019 Abstract: Floods are some of the most destructive and catastrophic disasters worldwide. Development of management plans needs a deep understanding of the likelihood and magnitude of future flood events.