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

• Main Authors: Pande C.B., Kushwaha N.L., Orimoloye I.R., Kumar R., Abdo H.G., Tolche A.D., Elbeltagi A.
• Other Authors: 57193547008
• Format: Article
• Published: Springer Science and Business Media B.V. 2024
• Subjects: 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
• Institution: Universiti Tenaga Nasional

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...