Climate Change Data Portal
| DOI | 10.1007/s11269-023-03440-0 |
| Comparative Assessment of Improved SVM Method under Different Kernel Functions for Predicting Multi-scale Drought Index | |
| Pande, Chaitanya B.; Kushwaha, N. L.; Orimoloye, Israel R.; Kumar, Rohitashw; Abdo, Hazem Ghassan; Tolche, Abebe Debele; Elbeltagi, Ahmed | |
| Date Issued | 2023 |
| ISSN | 0920-4741 |
| EISSN | 1573-1650 |
| startpage | 1367 |
| endpage | 1399 |
| Volume | 37Issue:3 |
| Other Abstract | 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 (R-2 = 0.819) and SPI-12 (R-2 = 0.968) values at Paithan station; the SPI-3 (R-2 = 0.736) and SPI-6 (R-2 = 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 (R-2 = 0.846) at Paithan station and SPI-12 (R-2 = 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. |
| enkeywords | SPI; Sensitivity analysis; Support vector machine; Best subset regression; Kernel functions |
| Language | 英语 |
| WOS Research Area | Engineering, Civil ; Water Resources |
| WOS Subject | Science Citation Index Expanded (SCI-EXPANDED) |
| WOS ID | WOS:000924491500003 |
| journal | WATER RESOURCES MANAGEMENT
 |
| Document Type | 期刊论文 |
| Identifier | http://gcip.llas.ac.cn/handle/2XKMVOVA/281606 |
| Affiliation | Ministry of Earth Sciences (MoES) - India; Indian Institute of Tropical Meteorology (IITM); Indian Council of Agricultural Research (ICAR); ICAR - Indian Agricultural Research Institute; University of the Free State; University West Indies Mona Jamaica; University West Indies Saint Augustine; Sher-e-Kashmir University of Agricultural Sciences & Technology of Kashmir (SKUAST Kashmir); Tartous University; Haramaya University; Egyptian Knowledge Bank (EKB); Mansoura University; Universiti Tenaga Nasional; Damascus University |
| Recommended Citation GB/T 7714 | Pande, Chaitanya B.,Kushwaha, N. L.,Orimoloye, Israel R.,et al. Comparative Assessment of Improved SVM Method under Different Kernel Functions for Predicting Multi-scale Drought Index[J],2023,37(3). |
| APA | Pande, Chaitanya B..,Kushwaha, N. L..,Orimoloye, Israel R..,Kumar, Rohitashw.,Abdo, Hazem Ghassan.,...&Elbeltagi, Ahmed.(2023).Comparative Assessment of Improved SVM Method under Different Kernel Functions for Predicting Multi-scale Drought Index.WATER RESOURCES MANAGEMENT,37(3). |
| MLA | Pande, Chaitanya B.,et al."Comparative Assessment of Improved SVM Method under Different Kernel Functions for Predicting Multi-scale Drought Index".WATER RESOURCES MANAGEMENT 37.3(2023). |
| Files in This Item: | There are no files associated with this item. | |||||
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
