Publication:
Assessing rainfall prediction models: Exploring the advantages of machine learning and remote sensing approaches

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dc.citedby16
dc.contributor.authorLatif S.D.en_US
dc.contributor.authorAlyaa Binti Hazrin N.en_US
dc.contributor.authorHoon Koo C.en_US
dc.contributor.authorLin Ng J.en_US
dc.contributor.authorChaplot B.en_US
dc.contributor.authorFeng Huang Y.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorNajah Ahmed A.en_US
dc.contributor.authorid57216081524en_US
dc.contributor.authorid58642255900en_US
dc.contributor.authorid58641117700en_US
dc.contributor.authorid57192698412en_US
dc.contributor.authorid57201316781en_US
dc.contributor.authorid55807263900en_US
dc.contributor.authorid16068189400en_US
dc.contributor.authorid58136810800en_US
dc.date.accessioned2024-10-14T03:17:40Z
dc.date.available2024-10-14T03:17:40Z
dc.date.issued2023
dc.description.abstractUsing a comparison of three different major types, the best predictive model was determined. Statistical models and machine learning algorithms automatically learn and improve based on data. Deep learning uses neural networks to learn complex data patterns and relationships. A combination of satellite imagery, radar data, and ground-based observations are used and using aircraft or satellites, and remote sensing (RS) collects data on distant objects or locations. Satellites and radar are used to gather regional precipitation data for hybrid models. An algorithm trained on historical rainfall measurements would then process the data. Using remote monitoring instrument input features, the machine-learning model can predict precipitation. Evaluation of machine learning regression methods is based on the degree of agreement between predicted and observed values. The RMSE, R2, and MAE statistical measures check on the precision of a prediction or forecasting model. Machine learning excels at rainfall prediction regardless of climate or timescale. As one of the more popular models for predicting rainfall, the LSTM models demonstrate their superiority. Remote sensing and hybrid predictive models should be investigated further due to their scarcity. � 2023 THE AUTHORSen_US
dc.description.natureFinalen_US
dc.identifier.doi10.1016/j.aej.2023.09.060
dc.identifier.epage25
dc.identifier.scopus2-s2.0-85173857230
dc.identifier.spage16
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85173857230&doi=10.1016%2fj.aej.2023.09.060&partnerID=40&md5=cd370191babf611b33890ad186710d8c
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34018
dc.identifier.volume82
dc.pagecount9
dc.publisherElsevier B.V.en_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofGold Open Access
dc.sourceScopus
dc.sourcetitleAlexandria Engineering Journal
dc.subjectHybrid models
dc.subjectMachine learning
dc.subjectPrediction
dc.subjectRainfall
dc.subjectRemote sensing
dc.subjectClimate models
dc.subjectForecasting
dc.subjectLearning algorithms
dc.subjectLearning systems
dc.subjectLong short-term memory
dc.subjectRain
dc.subjectRegression analysis
dc.subjectSatellite imagery
dc.subjectHybrid model
dc.subjectLearn+
dc.subjectMachine-learning
dc.subjectModel learning
dc.subjectPrediction modelling
dc.subjectPredictive models
dc.subjectRainfall prediction
dc.subjectRemote sensing approaches
dc.subjectRemote-sensing
dc.subjectStatistic modeling
dc.subjectRemote sensing
dc.titleAssessing rainfall prediction models: Exploring the advantages of machine learning and remote sensing approachesen_US
dc.typeReviewen_US
dspace.entity.typePublication
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