Prediction Success of Machine Learning Methods for Flash Flood Susceptibility Mapping in the Tafresh Watershed, Iran

Saeid Janizadeh, Mohammadtaghi Avand, Abolfazl Jaafari, Tran Van Phong, Mahmoud Bayat, Ebrahim Ahmadisharaf, Indra Prakash, Binh Thai Pham and Saro Lee
Additional contact information
Saeid Janizadeh: Department of Watershed Management Engineering, College of Natural Resources, Tarbiat Modares University, Tehran, P.O. Box 14115-111, Iran
Mohammadtaghi Avand: Department of Watershed Management Engineering, College of Natural Resources, Tarbiat Modares University, Tehran, P.O. Box 14115-111, Iran
Abolfazl Jaafari: Research Institute of Forests and Rangelands, Agricultural Research, Education, and Extension Organization (AREEO), Tehran 13185-116, Iran
Tran Van Phong: Institute of Geological Sciences, Vietnam Academy of Sciences and Technology, 84 Chua Lang Street, Dong da, Hanoi, 100000, Viet Nam
Mahmoud Bayat: Research Institute of Forests and Rangelands, Agricultural Research, Education, and Extension Organization (AREEO), Tehran 13185-116, Iran
Ebrahim Ahmadisharaf: DHI, Lakewood, CO 80228, USA
Indra Prakash: Department of Science & Technology, Bhaskarcharya Institute for Space Applications and Geo-Informatics (BISAG), Government of Gujarat, Gandhinagar 382007, India
Binh Thai Pham: Institute of Research and Development, Duy Tan University, Da Nang 550000, Viet Nam
Saro Lee: Geoscience Platform Research Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124, Gwahak-ro Yuseong-gu, Daejeon 34132, Korea

Sustainability, 2019, vol. 11, issue 19, 1-19

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. The purpose of this research was to estimate flash flood susceptibility in the Tafresh watershed, Iran, using five machine learning methods, i.e., alternating decision tree (ADT), functional tree (FT), kernel logistic regression (KLR), multilayer perceptron (MLP), and quadratic discriminant analysis (QDA). A geospatial database including 320 historical flood events was constructed and eight geo-environmental variables—elevation, slope, slope aspect, distance from rivers, average annual rainfall, land use, soil type, and lithology—were used as flood influencing factors. Based on a variety of performance metrics, it is revealed that the ADT method was dominant over the other methods. The FT method was ranked as the second-best method, followed by the KLR, MLP, and QDA. Given a few differences between the goodness-of-fit and prediction success of the methods, we concluded that all these five machine-learning-based models are applicable for flood susceptibility mapping in other areas to protect societies from devastating floods.

Keywords: alternating decision tree; data mining; spatial modeling; susceptibility mapping; GIS (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (20)

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