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Rainfall forecasting model using machine learning methods: Case study Terengganu, Malaysia

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dc.citedby68
dc.contributor.authorRidwan W.M.en_US
dc.contributor.authorSapitang M.en_US
dc.contributor.authorAziz A.en_US
dc.contributor.authorKushiar K.F.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57218502036en_US
dc.contributor.authorid57215211508en_US
dc.contributor.authorid57205233815en_US
dc.contributor.authorid57212462702en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T09:07:33Z
dc.date.available2023-05-29T09:07:33Z
dc.date.issued2021
dc.descriptionClimate change; Decision trees; Machine learning; Reservoirs (water); Water levels; Weather forecasting; Autocorrelation functions; Boosted decision trees; Coefficient of determination; Comparative studies; Forecasting modeling; Hyper-parameter; Machine learning methods; Rainfall forecasting; Rainen_US
dc.description.abstractRainfall plays a main role in managing the water level in the reservoir. The unpredictable amount of rainfall due to the climate change can cause either overflow or dry in the reservoir. In this study, several models and methods were applied to predict the rainfall data in Tasik Kenyir, Terengganu. The comparative study was conducted focusing on developing and comparing several Machine Learning (ML) models, evaluating different scenarios and time horizon, and forecasting rainfall using two types of methods. Data involved for this research consist of taking the average rainfall from 10 stations around the study area using Thiessen polygon to weight the station area and projected rainfall. The forecasting model uses four different ML algorithms, which are Bayesian Linear Regression (BLR), Boosted Decision Tree Regression (BDTR), Decision Forest Regression (DFR) and Neural Network Regression (NNR). On the other hand, the rainfall was predicted on different time horizon by using different ML's algorithms which is method 1 (M1): Forecasting Rainfall Using Autocorrelation Function (ACF) and method 2 (M2): Forecasting Rainfall Using Projected Error. In M1, the best regression developed for ACF is BDTR since it has the highest coefficient of determination, R2, after tuning the hyperparameter. The results show coefficient between 0.5 and 0.9 with the highest of each scenarios for daily (0.9739693), weekly (0.989461), 10-days (0.9894429) and monthly (0.9998085). In M2, overall model performances show that normalization using LogNormal is preferably giving a good result of each categories except for 10-days with BDTR and DFR are the most acceptable result than NNR and BLR. It is concluded that, two different methods have been applied with different scenarios and different time horizons, and M1 shows a rather high accuracy than M2 using BDTR modeling. � 2020 THE AUTHORSen_US
dc.description.natureFinalen_US
dc.identifier.doi10.1016/j.asej.2020.09.011
dc.identifier.epage1663
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85095868138
dc.identifier.spage1651
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85095868138&doi=10.1016%2fj.asej.2020.09.011&partnerID=40&md5=4c3cb64699a7f5b87cc8f0dd7ffee727
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26188
dc.identifier.volume12
dc.publisherAin Shams Universityen_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleAin Shams Engineering Journal
dc.titleRainfall forecasting model using machine learning methods: Case study Terengganu, Malaysiaen_US
dc.typeArticleen_US
dspace.entity.typePublication
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