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

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

This 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 predi...

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主要な著者:	Pande C.B., Kushwaha N.L., Orimoloye I.R., Kumar R., Abdo H.G., Tolche A.D., Elbeltagi A.
その他の著者:	57193547008
フォーマット:	論文
出版事項:	Springer Science and Business Media B.V. 2024
主題:	Best subset regression Kernel functions Sensitivity analysis SPI Support vector machine Godavari Basin India Drought Errors Forecasting Mean square error Optimization Radial basis function networks Support vector regression Vectors Comparative assessment Drought monitoring Kernel function Multi-scales Semi-arid region Sequential minimal optimization Standardized precipitation index Support vector machine models Support vectors machine climate prediction comparative study drought precipitation (climatology) regression analysis sensitivity analysis support vector machine
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Comparative Assessment of Improved SVM Method under Different Kernel Functions for Predicting Multi-scale Drought Index

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