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Advanced machine learning algorithms for flood susceptibility modeling — performance comparison: Red Sea, Egypt

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Abstract

Floods are among the most devastating environmental hazards that directly and indirectly affect people’s lives and activities. In many countries, sustainable environmental management requires the assessment of floods and the likely flood-prone areas to avoid potential hazards. In this study, the performance and capabilities of seven machine learning algorithms (MLAs) for flood susceptibility mapping were tested, evaluated, and compared. These MLAs, including support vector machine (SVM), random forest (RF), multivariate adaptive regression spline (MARS), boosted regression tree (BRT), functional data analysis (FDA), general linear model (GLM), and multivariate discriminant analysis (MDA), were tested for the area between Safaga and Ras Gharib cities, Red Sea, Egypt. A geospatial database was developed with eleven flood-related factors, namely altitude, slope aspect, lithology, land use/land cover (LULC), slope length (LS), topographic wetness index (TWI), slope angle, profile curvature, plan curvature, stream power index (SPI), and hydrolithology units. In addition, 420 actual flooded areas were recorded from the study area to create a flood inventory map. The inventory data were randomly divided into training group with 70% and validation group with 30%. The flood-related factors were tested with a multicollinearity test, the variance inflation factor (VIF) was less than 2.135, the tolerance (TOL) was more than 0.468, and their importance was evaluated with a partial least squares (PLS) method. The results show that RF performed the best with the highest AUC (area under curve) value of 0.813, followed by GLM with 0.802, MARS with 0.801, BRT with 0.777, MDA with 0.768%, FDA with 0.763, and SVM with 0.733. The results of this study and the flood susceptibility maps could be useful for environmental mitigation, future development activities in the area, and flood control areas.

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Acknowledgements

This work was supported by the Iran National Science Foundation (INSF) under Grant No. 99011055. Thanks to INSF.

Funding

This study was supported by the Iran National Science Foundation (INSF) under Grant No. 99011055. Thanks to INSF. 

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Authors and Affiliations

  1. Geology Department, Faculty of Science, Sohag University, Sohag, Egypt

    Ahmed M. Youssef & Bosy A. El-Haddad

  2. Geological Hazards Department, Applied Geology Sector, Saudi Geological Survey, P.O. Box 54141, Jeddah, 21514, Kingdom of Saudi Arabia

    Ahmed M. Youssef

  3. Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, Iran

    Hamid Reza Pourghasemi

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  1. Ahmed M. Youssef
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  3. Bosy A. El-Haddad
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AMY, HRP, and BAEl-H designed the experiments, ran models, analyzed the results, and wrote and reviewed the manuscript.

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Correspondence to Hamid Reza Pourghasemi.

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Youssef, A.M., Pourghasemi, H.R. & El-Haddad, B.A. Advanced machine learning algorithms for flood susceptibility modeling — performance comparison: Red Sea, Egypt. Environ Sci Pollut Res 29, 66768–66792 (2022). https://doi.org/10.1007/s11356-022-20213-1

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