Flood Prediction Using Machine Learning Models: Literature Review

TL;DR

This literature review analyzes the application of machine learning models in flood prediction, highlighting their performance, trends, and the most promising methods for both long-term and short-term flood forecasting.

Contribution

It provides a comprehensive overview of the state-of-the-art ML models in flood prediction, including benchmarking, performance analysis, and insights into effective strategies for model improvement.

Findings

ML models show high robustness and accuracy in flood prediction

Hybridization and ensemble methods enhance model performance

Data decomposition and optimization are key to improving prediction quality

Abstract

Floods are among the most destructive natural disasters, which are highly complex to model. The research on the advancement of flood prediction models contributed to risk reduction, policy suggestion, minimization of the loss of human life, and reduction the property damage associated with floods. To mimic the complex mathematical expressions of physical processes of floods, during the past two decades, machine learning (ML) methods contributed highly in the advancement of prediction systems providing better performance and cost-effective solutions. Due to the vast benefits and potential of ML, its popularity dramatically increased among hydrologists. Researchers through introducing novel ML methods and hybridizing of the existing ones aim at discovering more accurate and efficient prediction models. The main contribution of this paper is to demonstrate the state of the art of ML models in flood prediction and to give insight into the most suitable models. In this paper, the literature where ML models were benchmarked through a qualitative analysis of robustness, accuracy, effectiveness, and speed are particularly investigated to provide an extensive overview on the various ML algorithms used in the field. The performance comparison of ML models presents an in-depth understanding of the different techniques within the framework of a comprehensive evaluation and discussion. As a result, this paper introduces the most promising prediction methods for both long-term and short-term floods. Furthermore, the major trends in improving the quality of the flood prediction models are investigated. Among them, hybridization, data decomposition, algorithm ensemble, and model optimization are reported as the most effective strategies for the improvement of ML methods.