Evaluation of Machine Learning Approach in Flood Prediction Scenarios and Its Input Parameters: A Systematic Review

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dc.contributor.authorNur-Adib Maspo
dc.contributor.authorAizul Nahar bin Harun
dc.contributor.authorMasafumi Goto
dc.contributor.authorFaizah Cheros
dc.contributor.authorNuzul
dc.contributor.authorAzam Haron
dc.contributor.authorMohd Nasrun Mohd Nawi
dc.coverage.publicationMalaysia
dc.date.accessioned2024-05-20T01:10:59Z
dc.date.available2024-05-20T01:10:59Z
dc.date.issued2020
dc.description.abstractThis 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.
dc.identifier.citationMaspo, N. A., Harun, A. N. B., Goto, M., Cheros, F., Haron, N. A., & Nawi, M. N. M. (2020). Evaluation of Machine Learning approach in flood prediction scenarios and its input parameters: A systematic review. In IOP Conference Series: Earth and Environmental Science (Vol. 479, No. 1, p. 012038). IOP Publishing.
dc.identifier.urihttps://repoemc.ukm.my/handle/123456789/1172
dc.language.isoen
dc.publisherIOP Publishing
dc.publisher.alternativeIOP Conference Series: Earth and Environmental Science
dc.titleEvaluation of Machine Learning Approach in Flood Prediction Scenarios and Its Input Parameters: A Systematic Review
dc.typeJournal

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