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Comparative study: Using machine learning techniques about rainfall prediction

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dc.citedby0
dc.contributor.authorHasan R.A.en_US
dc.contributor.authorAlomari M.F.en_US
dc.contributor.authorJamaluddin J.B.en_US
dc.contributor.authorid58487876600en_US
dc.contributor.authorid57350402200en_US
dc.contributor.authorid58695052500en_US
dc.date.accessioned2024-10-14T03:17:47Z
dc.date.available2024-10-14T03:17:47Z
dc.date.issued2023
dc.description.abstractGlobal warming has an impact on people all around the globe, which has a substantial impact on them and is hastening climate change. An accurate forecasting system is required for early detection and enhanced agricultural land management. Rainfall forecasting is a challenging job in reality, and the findings must be precise. Rainfall forecasting is a major problem because of the unreliability of existing methods for predicting rainfall. The purpose of this paper to build an accurate model for the daily prediction of the rainfall in Australia using Python In order to find the optimal model based on testing accuracy, four machine learning algorithms are utilised for training and testing (Logistic Regression, Gaussian Naive Bayes, XGboost classifier, and Random Forest). The data was collected at a variety of meteorological stations around Australia using Kaggle. This data collection has 145460 records and 22 attributes. As a result, in order to find the best suitable technique, a comparative study was undertaken after applying four methodologies. Furthermore, a variety of techniques are used, including multiple linear regression and support vector regression, which may provide the most accurate results (up to 78 %). We observed that our model is less accurate when we compared it to this study. With an accuracy of 84.61 %, the XGBoost classifier surpasses rival approaches. There are several misconceptions, according to the matrix. It will be necessary to combine diverse methodologies in the future to increase prediction accuracy. In addition, new datasets are being used, and new areas are being explored. � 2023 Author(s).en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo50014
dc.identifier.doi10.1063/5.0148472
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85176736708
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85176736708&doi=10.1063%2f5.0148472&partnerID=40&md5=6cc5911327b4d9175dc6341ae57302cb
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34053
dc.identifier.volume2787
dc.publisherAmerican Institute of Physics Inc.en_US
dc.sourceScopus
dc.sourcetitleAIP Conference Proceedings
dc.titleComparative study: Using machine learning techniques about rainfall predictionen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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