Publication:
Machine learning techniques for flood forecasting

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dc.citedby2
dc.contributor.authorHadi F.A.A.en_US
dc.contributor.authorSidek L.M.en_US
dc.contributor.authorSalih G.H.A.en_US
dc.contributor.authorBasri H.en_US
dc.contributor.authorSammen S.Sh.en_US
dc.contributor.authorDom N.M.en_US
dc.contributor.authorAli Z.M.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorid59047541300en_US
dc.contributor.authorid35070506500en_US
dc.contributor.authorid59044731800en_US
dc.contributor.authorid57065823300en_US
dc.contributor.authorid57192093108en_US
dc.contributor.authorid57189070135en_US
dc.contributor.authorid59046989900en_US
dc.contributor.authorid57214837520en_US
dc.date.accessioned2025-03-03T07:43:33Z
dc.date.available2025-03-03T07:43:33Z
dc.date.issued2024
dc.description.abstractClimate change resulted in dramatic change in the monsoon precipitation rates in Malaysia, contributing to repetitive flooding events. This research examines different substantial practicalities of machine learning (ML) in performing high-performance and accurate FF. The case study was The Dungun River. IGISMAPs datasets of water level and rainfall were investigated (1986?2000). The Forecasting was implemented for current (1986?2000) and near future (2020?2030). ML algorithms were Logistic Regression, K-Nearest neighbors, Support Vector Classifier, Naive Bayes, Decision tree, Random Forest, and Artificial Neural Network. Simulations were run in the Colab software tool. The results revealed that between 1986 and 2000, there would be an average of (18?55) floods around the Dungun River Basin. Floods occurred rarely before 1985. They have been common since 2000. 35 floods occurred annually on average since 2000. It is predicted that between 2020 and 2030, flooding events would grow on the Dungun River Basin. Most floods occurred due to rainfall between 1 and 500 mm. The maximum frequency of flooding was measured at 110 occurrences at a rainfall of 250 mm. The overall accuracies were 75.61%/ random forest, 73.17%/ KNN, and logistic regression/ 48.78%. Overall, the ANN models had a competitive mean accuracy of 90.85%. ? 2024 The Authors.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.2166/hydro.2024.208
dc.identifier.epage799
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85192236473
dc.identifier.spage779
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85192236473&doi=10.2166%2fhydro.2024.208&partnerID=40&md5=28f3b2c4e4c3fbc92c78be5604f3c244
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36636
dc.identifier.volume26
dc.pagecount20
dc.publisherIWA Publishingen_US
dc.relation.ispartofAll Open Access; Gold Open Access
dc.sourceScopus
dc.sourcetitleJournal of Hydroinformatics
dc.titleMachine learning techniques for flood forecastingen_US
dc.typeArticleen_US
dspace.entity.typePublication
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