Publication:
Real-Time Flood Inundation Map Generation Using Decision Tree Machine Learning Method: Case Study of Kelantan River Basins

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dc.citedby0
dc.contributor.authorSidek L.M.en_US
dc.contributor.authorBasri H.en_US
dc.contributor.authorMarufuzzaman M.en_US
dc.contributor.authorDeros A.M.en_US
dc.contributor.authorOsman S.en_US
dc.contributor.authorHassan F.A.en_US
dc.contributor.authorid35070506500en_US
dc.contributor.authorid57065823300en_US
dc.contributor.authorid56976224000en_US
dc.contributor.authorid58905646500en_US
dc.contributor.authorid57189233135en_US
dc.contributor.authorid58905990100en_US
dc.date.accessioned2024-10-14T03:19:31Z
dc.date.available2024-10-14T03:19:31Z
dc.date.issued2023
dc.description.abstractSpatial investigation and rainfall affect the most in runoff and flood modelling compared to other influencing flood sources. Therefore, flash flood and short-term flood prediction require numerical rainfall estimation, which employs falls, mudflow, melted ice, etc. To forecast unexpected flood occurrences, faster flood prediction necessitates computational prediction models such as Machine Learning (ML) algorithms, which are extensively utilized around the world. So, in this research, a real-time flood inundation map (FIM) is used to develop the ML-based visualization (ML_V) method to characterize the collected dataset. Additionally, to predict the flood depth, a trained Decision Tree (DT)-based sorting algorithm is used in this method. This DT-based model takes 4 random rainfall data to train and predict the flood depth of the study areaen_US
dc.description.abstractthe Kelantan River basin in Malaysia, which needs further processing for ML_V. The results showed that the precision of the forecasted map was around 80% which is compared with another Gaussian Na�ve Bias ML algorithm. However, in imbrication with the ArcGIS maps, the forecasted map detached the �out of boundary� images and generated a clearer map. It is obvious that this ML_V model was anticipated to read the final output, which is involved during the flood guidance statement (FGS) to broadcast the data to the community. � The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/978-981-99-3708-0_1
dc.identifier.epage16
dc.identifier.scopus2-s2.0-85185928772
dc.identifier.spage1
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85185928772&doi=10.1007%2f978-981-99-3708-0_1&partnerID=40&md5=f571c5c84e911847efe0984486cca593
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34400
dc.identifier.volumePart F2265
dc.pagecount15
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.sourceScopus
dc.sourcetitleWater Resources Development and Management
dc.subjectDecision tree
dc.subjectFlood forecasting
dc.subjectFlood hazard map
dc.subjectFlood impacts
dc.subjectKelantan
dc.subjectMachine learning
dc.subjectRisk assessment
dc.titleReal-Time Flood Inundation Map Generation Using Decision Tree Machine Learning Method: Case Study of Kelantan River Basinsen_US
dc.typeBook chapteren_US
dspace.entity.typePublication
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