Maspo, Nur-Adib and Harun, Aizul Nahar and Goto, Masafumi and Cheros, Faizah and Haron, Nuzul Azam and Mohd Nawi, Mohd Nasrun (2020) Evaluation of Machine Learning approach in flood prediction scenarios and its input parameters: A systematic review. IOP Conference Series: Earth and Environmental Science, 479. pp. 1-15. ISSN 1755-1315

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Abstract

Flood disaster is a major disaster that frequently happens globally, it brings serious impacts to lives, property, infrastructure and environment. To stop flooding seems to be difficult but to prevent from serious damages that caused by flood is possible. Thus, implementing flood prediction could help in flood preparation and possibly to reduce the impact of flooding. This study aims to evaluate the existing machine learning (ML) approaches for flood prediction as well as evaluate parameters used for predicting flood, the evaluation is based on the review of previous research articles. In order to achieve the aim, this study is in two-fold; the first part is to identify flood prediction approaches specifically using ML methods and the second part is to identify flood prediction parameters that have been used as input parameters for flood prediction model. The main contribution of this paper is to determine the most recent ML techniques in flood prediction and identify the notable parameters used as model input so that researchers and/or flood managers can refer to the prediction results as the guideline in considering ML method for early flood prediction.

Item Type:	Article
Subjects:	H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management
Divisions:	School of Technology Management & Logistics
Depositing User:	Mrs. Norazmilah Yaakub
Date Deposited:	14 Apr 2021 06:19
Last Modified:	14 Apr 2021 06:19
URI:	https://repo.uum.edu.my/id/eprint/28272

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