Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/80357
Title: Flood prediction using machine learning models : literature review
Authors: Mosavi, A
Ozturk, P
Chau, KW 
Keywords: Adaptive neuro-fuzzy inference system (ANFIS)
Artificial intelligence
Artificial neural network
Big data
Classification and regression trees (CART), data science
Decision tree
Extreme event management
Flood forecasting
Flood prediction
Hybrid & ensemble machine learning
Hydrologic model
Natural hazards & disasters
Rainfall-runoff
Soft computing
Support vector machine
Survey
Time series prediction
Issue Date: 2018
Publisher: Molecular Diversity Preservation International (MDPI)
Source: Water, 2018, v. 10, no. 11, 1536 How to cite?
Journal: Water 
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. 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.
URI: http://hdl.handle.net/10397/80357
ISSN: 2073-4441
DOI: 10.3390/w10111536
Rights: © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
The following publication: Mosavi, A.; Ozturk, P.; Chau, K.-W. Flood Prediction Using Machine Learning Models: Literature Review. Water 2018, 10, 1536 is available at https://doi.org/10.3390/w10111536
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