Next Article in Journal
Sustainable Development of Power Retailers and Industrial Parks in China’s Belt and Road Initiative
Next Article in Special Issue
Performance Evaluation of the GIS-Based Data-Mining Techniques Decision Tree, Random Forest, and Rotation Forest for Landslide Susceptibility Modeling
Previous Article in Journal
Analysis of Network Structure of Urban Bike-Sharing System: A Case Study Based on Real-Time Data of a Public Bicycle System
Previous Article in Special Issue
Analysis of Land Surface Deformation in Chagan Lake Region Using TCPInSAR
Article

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

1
Department of Watershed Management Engineering, College of Natural Resources, Tarbiat Modares University, Tehran, P.O. Box 14115-111, Iran
2
Research Institute of Forests and Rangelands, Agricultural Research, Education, and Extension Organization (AREEO), Tehran 13185-116, Iran
3
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
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 alternating decision tree; data mining; spatial modeling; susceptibility mapping; GIS
Show Figures

Figure 1

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; Avand, Mohammadtaghi; Jaafari, Abolfazl; Phong, Tran V.; Bayat, Mahmoud; Ahmadisharaf, Ebrahim; Prakash, Indra; Pham, Binh T.; Lee, Saro. 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

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop