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Advanced machine learning algorithms for flood susceptibility modeling — performance comparison: Red Sea, Egypt by Ahmed M. Youssef; Hamid Reza Pourghasemi; Bosy A. El-Haddad is a Environmental Science article available to read on EtoBox.

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

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 r

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Author

Ahmed M. Youssef; Hamid Reza Pourghasemi; Bosy A. El-Haddad

Publisher

Springer Science and Business Media LLC

Published

2022

Language

EN

Field

Environmental Science (Physical Sciences)