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

Abstract

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

Utilizing Satellite Data to Establish Rainfall Intensity-Duration-Frequency Curves for Major Cities in Iraq

“…Jaber et al [59] also showed the efficacy of remote sensing precipitation in rainfall-runoff modelling in Iraq. Studies in the nearby region also showed the applicability of satellite rainfall in hydrological applications in Jordan [60], Iran [61], Saudi Arabia [62,63], and Turkey [64].…”

Understanding the impact of land use change on urban flood susceptibility mapping assessment: A review

“…However, the significance of models in various operational measures for hydrometeorological disasters has been delineated in the literature. Examples include the application of 2D models (Abdrabo et al, 2020), hydrodynamic models (Chen et al, 2021), statistical models (Lee et al, 2020), decision-making models (Cabrera and Lee, 2019;Taromideh et al, 2022), and machine-learning models (Pourghasemi et al, 2021;Al-Areeq et al, 2022;Li et al, 2022). Despite their ability to simulate flood susceptibility, these models exhibit several limitations, resulting in errors in their application and necessitating calibration stages in different regions.…”

Google Earth Engine and Machine Learning for Flash Flood Exposure Mapping—Case Study: Tetouan, Morocco

“…To evaluate a multiclass classification, the instances correctly and incorrectly classified for each category must be displayed in a very well-organized tabular representation called a confusion matrix of the predicted class labels against the actual class labels (Table 6). The classification product and associated validation sample can be cross-tabulated to determine a variety of metrics, such as overall accuracy (15), class producers' accuracy (16), class users' accuracy (17), and kappa statistic (18)…”

“…This was the case in the study of Shreevastav et al [15], which adopted the same approach to assess flood risk modeling in the lower Bagmati river region of Eastern Terai, Nepal, using the ML model of MaxEnt. Moreover, Al-Areeq et al's [16] research used a logistic model tree (LT), a Bagging ensemble (BE), k-nearest neighbors, and a kernel support vector machine to map Jeddah, Saudi Arabia's flood vulnerability. In Karachi, Pakistan, by combining a novel set model of Multi-Layer Perceptron, Support Vector Machine, and Logistic Regression (LR), which additionally assesses influencing variables, Yaseen et al [17] created a flood susceptibility map.…”