Flood potential mapping by integrating the bivariate statistics, multi-criteria decision-making, and machine learning techniques

Tabarestani, Ehsan Shahiri; Hadian, Sanaz; Pham, Quoc Bao; Ali, Sk Ajim; Phung, Dung

doi:10.1007/s00477-022-02342-8

Cited by 9 publications

(1 citation statement)

References 61 publications

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“…On the other hand, machine learning algorithms provide an objective and scalable approach to flood mapping [84][85][86]. Once the models are trained, they can be applied to large geographic areas, facilitating extensive flood susceptibility mapping [87,88]. Radar data can be effectively combined with other geospatial datasets, such as land-use maps, hydrological models, and historical flood records, to enhance flood susceptibility mapping.…”

Section: Discussionmentioning

confidence: 99%

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

Hitouri,

Mohajane,

Lahsaini

et al. 2024

Remote Sensing

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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.

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