Computational Machine Learning Approach for Flood Susceptibility Assessment Integrated with Remote Sensing and GIS Techniques from Jeddah, Saudi Arabia

• DOI: 10.3390/rs14215515
• Publication Date: 2022-11-03T07:53:07Z
• Authors: Ahmed Al-Areeq; S. Abba; Mohamed Yassin; Mohammed Benaafi; Mustafa Ghaleb; Isam Aljundi
• Keywords: Categorical variable; Flash flood

Floods, one of the most common natural hazards globally, are challenging to anticipate and estimate accurately. This study aims demonstrate predictive ability four ensemble algorithms for assessing flood risk. Bagging (BE), logistic model tree (LT), kernel support vector machine (k-SVM), k-nearest neighbour (KNN) used in this zoning Jeddah City, Saudi Arabia. The 141 locations have been identified research area based on interpretation aerial photos, historical data, Google Earth, field surveys. For purpose, 14 continuous factors different categorical examine their effect flooding area. dependency analysis (DA) was analyse strength predictors. comprises two input variables combination (C1 C2) features sensitivity selection. under-the-receiver operating characteristic curve (AUC) root mean square error (RMSE) were utilised determine accuracy a good forecast. validation findings showed that BE-C1 performed best terms precision, accuracy, AUC, specificity, as well lowest (RMSE). performance skills overall models proved reliable with range AUC (89–97%). can also be beneficial flash forecasts warning activity developed by disaster