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Flood Prediction Using Machine Learning Models: Literature Review

Water (Basel), 2018-10, Vol.10 (11), p.1536 [Peer Reviewed Journal]

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/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2073-4441 ;EISSN: 2073-4441 ;DOI: 10.3390/w10111536

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Title:
Flood Prediction Using Machine Learning Models: Literature Review
Author: Mosavi, Amir ; Ozturk, Pinar ; Chau, Kwok-wing
Subjects: Accuracy ; adaptive neuro-fuzzy inference system (ANFIS) ; Algorithms ; Artificial intelligence ; artificial neural network ; big data ; classification and regression trees (CART), data science ; Damage ; decision tree ; Disasters ; ensemble machine learning ; extreme event management ; Flood damage ; flood forecasting ; flood prediction ; Flood predictions ; Floods ; Hybridization ; hydrologic model ; Hydrology ; Learning algorithms ; Literature reviews ; Machine learning ; Mathematical models ; Methods ; Natural disasters ; natural hazards & ; Optimization ; Prediction models ; Property damage ; Qualitative analysis ; Rain ; rainfall–runoff, hybrid & ; Risk management ; Risk reduction ; Robustness (mathematics) ; soft computing ; Storm damage ; Stream flow ; support vector machine ; survey ; Surveying ; System effectiveness ; time series prediction
Is Part Of: Water (Basel), 2018-10, Vol.10 (11), p.1536
Description: 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.
Publisher: Basel: MDPI AG
Language: English
Identifier: ISSN: 2073-4441
EISSN: 2073-4441
DOI: 10.3390/w10111536
Source: GFMER Free Medical Journals
ROAD: Directory of Open Access Scholarly Resources
ProQuest Central
DOAJ Directory of Open Access Journals

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Flood Prediction Using Machine Learning Models: Literature Review

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