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 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 station