Flood Prediction Using Machine Learning Models: Literature Review: Comparison
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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.

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.

  • flood prediction
  • flood forecasting
  • hydrologic model
  • rainfall–runoff, hybrid & ensemble machine learnin
  • artificial neural network
  • support vector machine
  • natural hazards & disasters
  • adaptive neuro-fuzzy inference system (ANFIS)
  • decision tree
  • survey
  • classi
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