The Efficiency of an Ensemble Frequency Ratio-Support Vector Machine Model in the Detection of Flood-Prone Areas of the Kalat Basin

Document Type : Research Article


1 PhD Student, Department of Watershed Management, Sari Agricultural Sciences and Natural Resources University, Iran

2 Professor, Department of Watershed Management, Sari Agricultural Sciences and Natural Resources University, Iran

3 Associate Professor, Department of Watershed Management, Sari Agricultural Sciences and Natural Resources University, Iran

4 Professor, Centre for Advanced Modelling and Geospatial Information Systems, Faculty of Engineering and IT, University of Technology Sydney, NSW 2007, Australia


Flooding hurts the environment, economy, human communities, and industry. Therefore, comprehensive knowledge on flood probability modeling is essential to identify sensitive areas and to improve flood management systems. Advanced floods models usage has been grown dramatically today. That's why several researchers have integrated some models obtaining acceptable results for identifying flood-prone areas. Since numerous high-risk floods have occurred in the Kalat Basin and no advanced techniques have been used to estimate flood probability, so the Frequency Ratio-Support Vector Machine (FR-SVM) ensemble model was selected for flood modeling. Accuracy and efficiency evaluation, consequently, has been compared with the standalone SVM model. By investigation, 73 floods points were recorded according to recent 2018 end-month floods, and 15 conditioning factors including annual precipitation, geology, land use/land cover, slope length, river distance, analytical hill shading, elevation, convergence index, profile and plan curvatures, slope, stream power index, topographic roughness index, topographic wetness index and valley depth were considered. Models were evaluated by various precision criteria such as kappa coefficient, root means square errors, receiver operating characteristics and precision-recall curve. The FR-SVM model with a precision-recall curve of 0.8862 showed high accuracy and performance than SVM. These results can be used to manage flood-prone areas and other natural resource applications.


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Volume 7, Issue 1
April 2020
Pages 77-95
  • Receive Date: 07 August 2019
  • Revise Date: 09 February 2020
  • Accept Date: 09 February 2020
  • First Publish Date: 20 March 2020