The efficiency of an ensemble Frequency Ratio-Support Vector Machine model in the detection of flood proneable areas of Kalat Basin.

Flooding hurts the environment, economy, human communities, and industry. Therefore, comprehensive knowledge on flood probability modeling is essential to identify sensitive areas and 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 to flood-prone areas recognition. Since numerous high-risk floods have occurred in Kalat Basin and no advanced techniques have been used to flood probability mapping, so the Frequency Ratio-Support Vector Machine (FR-SVM) ensemble model was selected to 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 months floods, and 15 conditioning factors including annual precipitation, geology, land use/land cover, slope length, river distance, analytical hillshading, 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.