Modelisation Hydraulique Mono Dimensionnel Par HEC RAS, Application Sur L’oued Aggay (Ville De Sefrou)

The city of Sefrou, because of its geographical position, its cultural heritage and urban planning, than economically, is classified as one of the sites with a vulnerability particular to floods. Oued Aggay, the subject of this study, constitutes a danger potential because of the violence of its floods. In this perspective that comes this study that part of the creation and management of a spatial database on flood risk in the Sebou basin. It aims to spatialize the extent of the floods of Oued Aggay and propose solutions to protection the city of Sefrou against floods. The chosen approach goes through a hydrological study, the choice of profiles and the construction of onedimensional model from HEC RAS hydrology software. This study allowed us to simulate floods by statistical methods, identify flood zones and determine the different water levels in the flooded area for the Oued Aggay watershed

Flood Susceptibility Mapping Using SAR Data and Machine Learning Algorithms in a Small Watershed in Northwestern Morocco

Flood susceptibility mapping plays a crucial role in flood risk assessment and management. Accurate identification of areas prone to flooding is essential for implementing effective mitigation measures and informing decision-making processes. In this regard, the present study used high-resolution remote sensing products, i.e., synthetic aperture radar (SAR) images for flood inventory preparation and integrated four machine learning models (Random Forest: RF, Classification and Regression Trees: CART, Support Vector Machine: SVM, and Extreme Gradient Boosting: XGBoost) to predict flood susceptibility in Metlili watershed, Morocco. Initially, 12 independent variables (elevation, slope angle, aspect, plan curvature, topographic wetness index, stream power index, distance from streams, distance from roads, lithology, rainfall, land use/land cover, and normalized vegetation index) were used as conditioning factors. The flood inventory dataset was divided into 70% and 30% for training and validation purposes using a popular library, scikit-learn (i.e., train_test_split) in Python programming language. Additionally, the area under the curve (AUC) was used to evaluate the performance of the models. The accuracy assessment results showed that RF, CART, SVM, and XGBoost models predicted flood susceptibility with AUC values of 0.807, 0.780, 0.756, and 0.727, respectively. However, the RF model performed better at flood susceptibility prediction compared to the other models applied. As per this model, 22.49%, 16.02%, 12.67%, 18.10%, and 31.70% areas of the watershed are estimated as being very low, low, moderate, high, and very highly susceptible to flooding, respectively. Therefore, this study showed that the integration of machine learning models with radar data could have promising results in predicting flood susceptibility in the study area and other similar environments