Flood Prediction Using Machine Learning Models: Literature Review

By Amir Mosavi, Pınar Öztürk, Kwok-Wing Chau. Water, 2018

1,396 citations recorded by the source corpus.

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 of 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. This survey can be used as a guideline for hydrologists as well as climate scientists in choosing the proper ML method according to the prediction task.

Subfields: Artificial Intelligence, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/2b27bc25ae40a32a63945be35752364ade6e6417

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Citations in the corpus

A review of machine learning applications in wildfire science and management (2020, background): “(2018), extreme weather prediction McGovern et al. (2017), flood forecasting Mosavi et al. (2018), statistical downscaling Vandal, Kodra, and Ganguly (2018), remote sensing Lary, Alavi, Gandomi, and Walker (2016), and water resources Shen (2018); Sun and Scanlon (2019).”
Process‐Guided Deep Learning Predictions of Lake Water Temperature (2019, background): “Many in the environmental sciences have called on the community to embrace machine learning as a powerful predictive tool (Hampton et al., 2013; Mosavi et al., 2018; Olden et al., 2008), and build theoretical knowledge directly into these models (Karpatne et al., 2017; Shen et al., 2018).”
A Rainfall‐Runoff Model With LSTM‐Based Sequence‐to‐Sequence Learning (2020, methodology): “Although robust ANN model development approaches are still needed (Maier et al., 2010), studies have shown that ANNs can predict the runoff more accurately than can the classical regression models (Mosavi et al., 2018; Riad et al., 2004).”