Abstract
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 = 0.99, Accuracy = 96%, Kappa statistics = 0.92) has the highest performance, followed by ANN (AUC = 0.98, Accuracy = 95%, Kappa statistics = 0.89) and SVM (AUC = 0.96, Accuracy = 92%, Kappa statistics = 0.80). The proposed approach is an effective tool for forecasting and predicting FF that can help reduce the severity of this disaster.
Highlights
Scientists believe that there is a strong link between the industrialization and global warming (McGregor et al, 2016), and that’s translate by many imbalances in the actual ecosystem
It is clear that the very high probability classes contain the largest number of training points that were extracted from the field survey that was done at the time of the flash flood
Further analysis in 3D mode generating using Random Forest (RF) model, as shown in Figure 5, shows that the entire city is at risk of being drowned, which confirms the information that the city is located between two mountains
Summary
Scientists believe that there is a strong link between the industrialization and global warming (McGregor et al, 2016), and that’s translate by many imbalances in the actual ecosystem. Global warming means rising in air and water temperature (Lykhovyd, 2018) that lead to numerous disasters such as storms, heat waves (Woolf and Wolf, 2013), forest fires (Molfetta et al, 2007) , droughts (Leng et al, 2015) and flash floods (Trenberth, 2008). Their consequences either economic, human or environmental are fatal, in terms of mortality, Floods and flash floods are considered the most severe in the world and have affected more than 2 billion people worldwide. Many studies have appeared in recent years, for example, but not limited to, the study of (Elmahdy et al, 2020) that uses land use/land cover (LULC), Lithology , Slope , Altitude , Plan curvature ,Relief , Stream networks , Stream density and distance from streams as factors , that they were introduced into the boosted regression tree (BRT), classification and regression trees (CART), and naive Bayes tree (NBT) algorithms to determine FF susceptibility in the United Arab Emirates (NUAE) .Another study in Egypt uses boosted regression tree (BRT), functional data analysis (FDA), general linear model (GLM), and multivariate discriminant analysis (MDA) based on nine factors ,including slope ,altitude, distance from main river , LU/LC, lithological units, curvature, aspect, and topographic wetness index (El-Haddad et al, 2021).In addition , the three state-of-the-art Artificial Neural Network (ANN), Random Forest (RF), and Support Vector Machine (SVM) coupled with Random Subspace (RS) were trained based on elevation, curvature, aspect, slope, topographic roughness index (TRI), topographic wetness index (TWI), stream power index (SPI), sediment transport index (STI), land use/land cover (LULC), distance to the river, soil type, and rainfall to map flood prone areas in Bangladesh
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