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Combination of data-driven models and best subset regression for predicting the standardized precipitation index (SPI) at the Upper Godavari Basin in India

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dc.citedby17
dc.contributor.authorPande C.B.en_US
dc.contributor.authorCostache R.en_US
dc.contributor.authorSammen S.S.en_US
dc.contributor.authorNoor R.en_US
dc.contributor.authorElbeltagi A.en_US
dc.contributor.authorid57193547008en_US
dc.contributor.authorid55888132500en_US
dc.contributor.authorid57192093108en_US
dc.contributor.authorid57221282650en_US
dc.contributor.authorid57204724397en_US
dc.date.accessioned2024-10-14T03:18:56Z
dc.date.available2024-10-14T03:18:56Z
dc.date.issued2023
dc.description.abstractStandardized precipitation index prediction and monitoring are essential to mitigating the effect of drought actions on precision farming, environments, climate-smart agriculture, and the water cycle. In this study, four data-driven models, additive regression, random subspace, M5Pruned (M5P), and bagging tree models, were adopted to predict the standardized precipitation index (SPI) at the Upper Godavari Basin for various periods (3�months, 6�months, and 12�months). The data-driven models� input data was pre-processed with machine learning models to increase quality and the model�s performance a priori. These four models predicted the SPI-3, SPI-6, and SPI-12�months based on three metrological station data. Based on the statistical performance metrics such as correlation coefficient (R), root mean square error (RMSE), mean absolute error (MAE), relative absolute error (RAE), and root relative squared error (RRSE), our findings showed that the bagging was the best model for predicting SPI-3 and SPI-6 while the M5P the best for SPI-12 estimation in station 1, while in stations 2 and 3, M5P was superlative in predicting the SPI-3 and SPI-12�months, and the bagging was best in SPI-6. All the best models had acceptable mid-term drought forecasting based on the SPI-3, SPI-6, and SPI-12�months for three stations in the Upper Godavari Basin in India. The machine learning models created in this study produced satisfactory results in short-term and mid-term drought forecasting, and it will be a new strategy for water developers and planners to use for future management and scheduling. � 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/s00704-023-04426-z
dc.identifier.epage558
dc.identifier.issue1-Feb
dc.identifier.scopus2-s2.0-85150652830
dc.identifier.spage535
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85150652830&doi=10.1007%2fs00704-023-04426-z&partnerID=40&md5=75764d6178a9da91c7be314ff1e6b5ac
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34305
dc.identifier.volume152
dc.pagecount23
dc.publisherSpringeren_US
dc.sourceScopus
dc.sourcetitleTheoretical and Applied Climatology
dc.subjectGodavari Basin
dc.subjectIndia
dc.subjectclimate modeling
dc.subjectclimate prediction
dc.subjectdrought
dc.subjectmachine learning
dc.subjectregression analysis
dc.titleCombination of data-driven models and best subset regression for predicting the standardized precipitation index (SPI) at the Upper Godavari Basin in Indiaen_US
dc.typeArticleen_US
dspace.entity.typePublication
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