Journal Article

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

Hitouri, Sliman, Setargie, Tadesual Asamin, Mohajane, Meriame, D’Antonio, Paola, Varasano, Antonietta, Singh, Suraj Kumar, Lahsaini, Meriam, Tripathi, Gaurav, Ali, Sk Ajim

2024

Overview

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.

• Publisher: MDPI AG
• Subject: Accuracy / Algorithms / Artificial intelligence / Artificial satellites in remote sensing / CART / Community / data collection / Decision making / Decision trees / Emergency preparedness / Environmental risk / Fatalities / Flood forecasting / Flood management / Flood mapping / Flood predictions / flood susceptibility / Flooding / Floods / Hydrology / Independent variables / inventories / Land cover / Land use / Landslides & mudslides / Learning algorithms / Lithology / Machine learning / Mapping / Methods / Model accuracy / Morocco / Neural networks / prediction / Programming languages / Property damage / Python / Radar / Radar data / radar image / Radar imaging / Rain / Rain and rainfall / Rainfall / random forest / Regression analysis / Remote sensing / Risk assessment / Storm damage / streams / Support vector machines / Susceptibility / SVM / Synthetic aperture radar / Topography / Vegetation index / Watersheds / XGBoost