  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.
