An Improved Flood Susceptibility Assessment in Jeddah, Saudi Arabia, Using Advanced Machine Learning Techniques

• Published in: Water (Basel) Vol. 15; no. 14; p. 2511
• Main Authors: Ghanim, Abdulnoor A. J., Shaf, Ahmad, Ali, Tariq, Zafar, Maryam, Al-Areeq, Ahmed M., Alyami, Saleh H., Irfan, Muhammad, Rahman, Saifur
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-07-2023
• Subjects: Accuracy; Algorithms; Classification; Climate change; Coastal plains; Data mining; Datasets; Deep learning; Flood forecasting; flood prediction; Floods; Forecasting; Forecasts and trends; Geographic information systems; Geospatial data; GIS; Hydrology; Jeddah flood; Land use; Machine learning; Methods; Neural networks; Partial differential equations; regression; Saudi Arabia; Storm damage; Time series; Topography; Watersheds; Saudi Arabia
• ISSN: 2073-4441; 2073-4441
• DOI: 10.3390/w15142511

The city of Jeddah experienced a severe flood in 2020, resulting in loss of life and damage to property. In such scenarios, a flood forecasting model can play a crucial role in predicting flood events and minimizing their impact on communities. The proposed study aims to evaluate the performance of machine learning algorithms in predicting floods and non-flood regions, including Gradient Boosting, Extreme Gradient Boosting, AdaBoosting Gradient, Random Forest, and the Light Gradient Boosting Machine, using the dataset from Jeddah City, Saudi Arabia. This study identified fourteen continuous parameters and various classification variables to assess the correlation between these variables and flooding incidents in the analyzed region. The performance of the proposed algorithms was measured using classification matrices and regression matrices. The highest accuracy (86%) was achieved by the Random Forest classifier, and the lowest error rate (0.06) was found with the Gradient Boosting regressor machine. The performance of other algorithms was also exceptional compared to existing literature. The results of the study suggest that the application of these machine learning algorithms can significantly enhance flood prediction accuracy, enabling various industries and sectors to make more informed decisions.