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

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, usi...

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Bibliographic Details
Published in:Sustainability 2019-10, Vol.11 (19), p.5426
Main Authors: Janizadeh, Saeid, Avand, Mohammadtaghi, Jaafari, Abolfazl, Phong, Tran Van, Bayat, Mahmoud, Ahmadisharaf, Ebrahim, Prakash, Indra, Pham, Binh Thai, Lee, Saro
Format: Article
Language:English
Subjects:
Annual rainfall
Datasets
Decision making
Disaster management
Discriminant analysis
Emergency preparedness
Flash floods
Flood management
Flood mapping
Flood predictions
Floods
Goodness of fit
Hydraulics
Hydrologic data
Land use
Learning algorithms
Lithology
Machine learning
Mapping
Multilayer perceptrons
Natural resources
Rivers
Runoff
Soils
Sustainability
Watershed management
Watersheds
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Summary: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.
ISSN:2071-1050
2071-1050
DOI:10.3390/su11195426