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Comparative Assessment of Improved SVM Method under Different Kernel Functions for Predicting Multi-scale Drought Index

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dc.citedby28
dc.contributor.authorPande C.B.en_US
dc.contributor.authorKushwaha N.L.en_US
dc.contributor.authorOrimoloye I.R.en_US
dc.contributor.authorKumar R.en_US
dc.contributor.authorAbdo H.G.en_US
dc.contributor.authorTolche A.D.en_US
dc.contributor.authorElbeltagi A.en_US
dc.contributor.authorid57193547008en_US
dc.contributor.authorid57219726089en_US
dc.contributor.authorid57196487246en_US
dc.contributor.authorid21834485900en_US
dc.contributor.authorid57193090158en_US
dc.contributor.authorid57198446685en_US
dc.contributor.authorid57204724397en_US
dc.date.accessioned2024-10-14T03:19:41Z
dc.date.available2024-10-14T03:19:41Z
dc.date.issued2023
dc.description.abstractThis paper focus on the drought monitoring and forecasting for semi-arid region based on the various machine learning models and SPI index. Drought phenomena are crucial role in the agriculture and drinking purposes in the area. In this study, Standardized Precipitation Index (SPI) was used to predicted the future drought in the upper Godavari River basin, India. We have selected the ten input combinations of ML model were used to prediction of drought for three SPI timescales (i.e., SPI -3, SPI-6, and SPI-12). The historical data of SPI from 2000 to 2019 was used for creation of ML models SPI prediction, these datasets was divided into training (75% of the data) and testing (25% of the data) models. The best subset regression method and sensitivity analysis were applied to estimate the most effective input variables for estimation of SPI 3, 6, and 12. The improved support vector machine model using sequential minimal optimization (SVM-SMO) with various kernel functions i.e., SMO-SVM poly kernel, SMO-SVM Normalized poly kernel, SMO-SVM PUK (Pearson Universal Kernel) and SMO-SVM RBF (radial basis function) kernel was developed to forecasting of the SPI-3,6 and 12�months. The ML models accuracy were compared with various statistical indicators i.e., root mean square error (RMSE), mean absolute error (MAE), relative absolute error (RAE), root relative squared error (RRSE), and correlation coefficient (r). The results of study area have been showed that the SMO-SVM poly kernel model precisely predicted the SPI-3 (R2 = 0.819) and SPI-12 (R2 = 0.968) values at Paithan stationen_US
dc.description.abstractthe SPI-3 (R2 = 0.736) and SPI-6 (R2 = 0.841) values at Silload station, respectively. The SMO-SVM PUK kernel is found that the best ML model for the prediction of SPI-6 (R2 = 0.846) at Paithan station and SPI-12 (R2 = 0.975) at the Silload station. The compared with SVM-SMO poly kernel and SVM-SMO PUK kernel was observed, these models are best forecasting of drought (i.e. SPI-6 and SPI-12), while SVM-SMO poly kernel is good for SPI-3 prediction at both stations. The results have been showed the ability of the SVM-SMO algorithm with various kernel functions successfully applied for the forecasting of multiscale SPI under the climate changes. It can be helpful for decision making in water resource management and tackle droughts in the semi-arid region of central India. � 2023, The Author(s), under exclusive licence to Springer Nature B.V.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/s11269-023-03440-0
dc.identifier.epage1399
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85147346153
dc.identifier.spage1367
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85147346153&doi=10.1007%2fs11269-023-03440-0&partnerID=40&md5=e3511bd1c866cd702a66683c2a3c9768
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34425
dc.identifier.volume37
dc.pagecount32
dc.publisherSpringer Science and Business Media B.V.en_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofGreen Open Access
dc.sourceScopus
dc.sourcetitleWater Resources Management
dc.subjectBest subset regression
dc.subjectKernel functions
dc.subjectSensitivity analysis
dc.subjectSPI
dc.subjectSupport vector machine
dc.subjectGodavari Basin
dc.subjectIndia
dc.subjectDrought
dc.subjectErrors
dc.subjectForecasting
dc.subjectMean square error
dc.subjectOptimization
dc.subjectRadial basis function networks
dc.subjectSupport vector regression
dc.subjectVectors
dc.subjectBest subset regression
dc.subjectComparative assessment
dc.subjectDrought monitoring
dc.subjectKernel function
dc.subjectMulti-scales
dc.subjectSemi-arid region
dc.subjectSequential minimal optimization
dc.subjectStandardized precipitation index
dc.subjectSupport vector machine models
dc.subjectSupport vectors machine
dc.subjectclimate prediction
dc.subjectcomparative study
dc.subjectdrought
dc.subjectprecipitation (climatology)
dc.subjectregression analysis
dc.subjectsensitivity analysis
dc.subjectsupport vector machine
dc.subjectSensitivity analysis
dc.titleComparative Assessment of Improved SVM Method under Different Kernel Functions for Predicting Multi-scale Drought Indexen_US
dc.typeArticleen_US
dspace.entity.typePublication
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