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

Janizadeh, Saeid; Avand, Mohammadtaghi; Jaafari, Abolfazl; Phong, Tran Van; Bayat, Mahmoud; Ahmadisharaf, Ebrahim; Prakash, Indra; Pham, Binh Thai; Lee, Saro

doi:10.3390/su11195426

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

¹,

¹,

⁴

⁵,

^6,*

and

Saro Lee

^7,8,*

Department of Watershed Management Engineering, College of Natural Resources, Tarbiat Modares University, Tehran, P.O. Box 14115-111, Iran

Research Institute of Forests and Rangelands, Agricultural Research, Education, and Extension Organization (AREEO), Tehran 13185-116, Iran

Institute of Geological Sciences, Vietnam Academy of Sciences and Technology, 84 Chua Lang Street, Dong da, Hanoi, 100000, Viet Nam

⁴

DHI, Lakewood, CO 80228, USA

⁵

Department of Science & Technology, Bhaskarcharya Institute for Space Applications and Geo-Informatics (BISAG), Government of Gujarat, Gandhinagar 382007, India

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

Authors to whom correspondence should be addressed.

Sustainability 2019, 11(19), 5426; https://doi.org/10.3390/su11195426

Received: 7 September 2019 / Revised: 25 September 2019 / Accepted: 29 September 2019 / Published: 30 September 2019

(This article belongs to the Special Issue Sustainable Applications of Remote Sensing and Geospatial Information Systems to Earth Observations)

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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. View Full-Text

Keywords: alternating decision tree; data mining; spatial modeling; susceptibility mapping; GIS

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MDPI and ACS Style

Janizadeh, S.; Avand, M.; Jaafari, A.; Phong, T.V.; Bayat, M.; Ahmadisharaf, E.; Prakash, I.; Pham, B.T.; Lee, S. Prediction Success of Machine Learning Methods for Flash Flood Susceptibility Mapping in the Tafresh Watershed, Iran. Sustainability 2019, 11, 5426. https://doi.org/10.3390/su11195426

AMA Style

Janizadeh S, Avand M, Jaafari A, Phong TV, Bayat M, Ahmadisharaf E, Prakash I, Pham BT, Lee S. Prediction Success of Machine Learning Methods for Flash Flood Susceptibility Mapping in the Tafresh Watershed, Iran. Sustainability. 2019; 11(19):5426. https://doi.org/10.3390/su11195426

Chicago/Turabian Style

Janizadeh, Saeid, Mohammadtaghi Avand, Abolfazl Jaafari, Tran V. Phong, Mahmoud Bayat, Ebrahim Ahmadisharaf, Indra Prakash, Binh T. Pham, and Saro Lee. 2019. "Prediction Success of Machine Learning Methods for Flash Flood Susceptibility Mapping in the Tafresh Watershed, Iran" Sustainability 11, no. 19: 5426. https://doi.org/10.3390/su11195426

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

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