Abstract: Flooding, caused by the overflow of water bodies beyond their natural boundaries, has severe environmental and socioeconomic consequences. To effectively predict and mitigate flood events, accurate and reliable flood modeling techniques are essential. This study provides a comprehensive review of the latest modeling techniques used in flood prediction, classifying them into two main categories: hydrologic models and machine learning models based on artificial intelligence. By objectively assessing the advantages and disadvantages of each model type, we aim to synthesize a systematic analysis of the various flood modeling approaches in the current literature. Additionally, we explore the potential of hybrid strategies that combine both modeling methods' best characteristics to develop more effective flood control measures. Our findings provide valuable insights for researchers and practitioners in the field of flood modeling, and our recommendations can contribute to the development of more efficient and accurate flood prediction systems.

A Fares Hamad Aljohani, Ahmad. B. Alkhodre, Adnan Ahamad Abi Sen, Muhammad Sher Ramazan, Bandar Alzahrani and Muhammad Shoaib Siddiqui, “Flood Prediction using Hydrologic and ML-based Modeling: A Systematic Review” International Journal of Advanced Computer Science and Applications(IJACSA), 14(11), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0141155

@article{Aljohani2023,
title = {Flood Prediction using Hydrologic and ML-based Modeling: A Systematic Review},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0141155},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0141155},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {11},
author = {A Fares Hamad Aljohani and Ahmad. B. Alkhodre and Adnan Ahamad Abi Sen and Muhammad Sher Ramazan and Bandar Alzahrani and Muhammad Shoaib Siddiqui}
}