Performance of machine learning algorithms for mapping and forecasting of flash flood susceptibility in tetouan, morocco

Abstract

Since the industrial revolution, the world is experiencing a huge change in its climate, which causes many imbalances such as flash floods (FF). The aim of this study is to propose a new approach for detection and forecasting of flash flood susceptibility in the city of Tetouan, Morocco. For this regard, support vector machine (SVM), logistic regression (LR), random forest (RF), Naïve Bayes (NB) and Artificial neural network (ANN) are used based on 1101 points (680 flood points and 421 non-flood points) and 9 flash-flood predictors (Elevation , Slope , Aspect , LU/LC , Stream Power Index , Plan curvature , Profile Curvature , Topographic Position Index and Topographic Wetness Index ) that were extracted from the DEM (10m resolution) and satellite imagery (Sentinel 2B) of the study area . Models were trained on 70% and tested on 30% of this dataset also they were evaluated using several metrics such as the Receiver Operating Characteristic (ROC) Curve, precision, recall, score and kappa index. The result demonstrated that RF (AUC Combining double low line 0.99, Accuracy Combining double low line 96%, Kappa statistics Combining double low line 0.92) has the highest performance, followed by ANN (AUC Combining double low line 0.98, Accuracy Combining double low line 95%, Kappa statistics Combining double low line 0.89) and SVM (AUC Combining double low line 0.96, Accuracy Combining double low line 92%, Kappa statistics Combining double low line 0.80). The proposed approach is an effective tool for forecasting and predicting FF that can help reduce the severity of this disaster.