Forecasting of meteorological drought using ensemble and machine learning models
This study highlights drought forecasting for understanding the semi-arid area in India, where drought phenomena play vital role in the irrigation, drinking water supplies, and sustaining the ecological with economic balance for every nation. Therefore, drought forecasting is important for the futur...
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my.uniten.dspace-362402025-03-03T15:41:40Z Forecasting of meteorological drought using ensemble and machine learning models Pande C.B. Sidek L.M. Varade A.M. Elkhrachy I. Radwan N. Tolche A.D. Elbeltagi A. 57193547008 35070506500 24173661000 55481426800 56763877500 57198446685 57204724397 India Drought Evapotranspiration Sensitivity analysis Climate Energy Gaussian process regression model Input variables Machine learning models Meteorological drought Modeling accuracy Semiarid area Standardized precipitation evapotranspiration Standardized precipitation index conservation planning drinking water drought evapotranspiration forecasting method precipitation intensity prediction regression analysis semiarid region Support vector regression This study highlights drought forecasting for understanding the semi-arid area in India, where drought phenomena play vital role in the irrigation, drinking water supplies, and sustaining the ecological with economic balance for every nation. Therefore, drought forecasting is important for the future drought planning based on the machine learning (ML) models. Hence, The Standardized Precipitation Index (SPI) at 3- and 6-month periods have been selected and used for future drought forecasting scenarios in area. The combinations of ten inputs SPI-1- and SPI-10 were used for predicting modeling for SPI-3 and SPI-6 timescales, that modeling developed based on the historical SPI datasets from 1989 to 2019�years. The SPI-3 and SPI-6 maximum and minimum values are shown SPI-3 (2.03 and -5.522) and SPI-6 (1.94 and -6.93). The SPI is a popular method for estimating the drought analysis and has been used everywhere at global level. The developed models have been compared with each other, with the best combination of input variables selected using subset regression models and sensitivity studies. After that, the active input parameters were used for forecasting of SPI-3 and SPI-6 values to understanding of drought in semi-arid area. The finest input variables combination have been used in the Ml models and established the novel five models such as robust linear regression, bagged trees, boosted trees, support vector regression (SVM-Linear), and Matern Gaussian Process Regression (Matern GPR) models. Such kind of models first time has been applied for the forecasting of future drought conditions. Whole models were fine and improved modeling by using hyperparameters tuning, bagging, and boosting models. Entire ML models? accuracy was compared using different statistical metrics. Compared with five ML models accuracy, we have found that the Matern GPR model better accuracy than other ML models. The best model accuracy is R2 = 0.95 and 0.93, RMSE, MSE, MAE, MARE, and NSE values, respectively, for predicting SPI-3 and SPI-6 values in the area. Therefore, the Matern GPR model was identified as the finest ML algorithm for predicting SPI-3 and SPI-6 associated with other algorithms. This research demonstrates the Matern GPR model's efficacy in predicting multiscale SPI-3 and SPI-6 under climate variations. It can be helpful in soil and water resource conservation planning and management and understanding droughts in the entire basin areas of the country India. ? The Author(s) 2024. Final 2025-03-03T07:41:40Z 2025-03-03T07:41:40Z 2024 Article 10.1186/s12302-024-00975-w 2-s2.0-85203527756 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203527756&doi=10.1186%2fs12302-024-00975-w&partnerID=40&md5=fd2619b525b59fd9641dcf92f94c6f16 https://irepository.uniten.edu.my/handle/123456789/36240 36 1 160 All Open Access; Gold Open Access Springer Scopus |
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India Drought Evapotranspiration Sensitivity analysis Climate Energy Gaussian process regression model Input variables Machine learning models Meteorological drought Modeling accuracy Semiarid area Standardized precipitation evapotranspiration Standardized precipitation index conservation planning drinking water drought evapotranspiration forecasting method precipitation intensity prediction regression analysis semiarid region Support vector regression |
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India Drought Evapotranspiration Sensitivity analysis Climate Energy Gaussian process regression model Input variables Machine learning models Meteorological drought Modeling accuracy Semiarid area Standardized precipitation evapotranspiration Standardized precipitation index conservation planning drinking water drought evapotranspiration forecasting method precipitation intensity prediction regression analysis semiarid region Support vector regression Pande C.B. Sidek L.M. Varade A.M. Elkhrachy I. Radwan N. Tolche A.D. Elbeltagi A. Forecasting of meteorological drought using ensemble and machine learning models |
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This study highlights drought forecasting for understanding the semi-arid area in India, where drought phenomena play vital role in the irrigation, drinking water supplies, and sustaining the ecological with economic balance for every nation. Therefore, drought forecasting is important for the future drought planning based on the machine learning (ML) models. Hence, The Standardized Precipitation Index (SPI) at 3- and 6-month periods have been selected and used for future drought forecasting scenarios in area. The combinations of ten inputs SPI-1- and SPI-10 were used for predicting modeling for SPI-3 and SPI-6 timescales, that modeling developed based on the historical SPI datasets from 1989 to 2019�years. The SPI-3 and SPI-6 maximum and minimum values are shown SPI-3 (2.03 and -5.522) and SPI-6 (1.94 and -6.93). The SPI is a popular method for estimating the drought analysis and has been used everywhere at global level. The developed models have been compared with each other, with the best combination of input variables selected using subset regression models and sensitivity studies. After that, the active input parameters were used for forecasting of SPI-3 and SPI-6 values to understanding of drought in semi-arid area. The finest input variables combination have been used in the Ml models and established the novel five models such as robust linear regression, bagged trees, boosted trees, support vector regression (SVM-Linear), and Matern Gaussian Process Regression (Matern GPR) models. Such kind of models first time has been applied for the forecasting of future drought conditions. Whole models were fine and improved modeling by using hyperparameters tuning, bagging, and boosting models. Entire ML models? accuracy was compared using different statistical metrics. Compared with five ML models accuracy, we have found that the Matern GPR model better accuracy than other ML models. The best model accuracy is R2 = 0.95 and 0.93, RMSE, MSE, MAE, MARE, and NSE values, respectively, for predicting SPI-3 and SPI-6 values in the area. Therefore, the Matern GPR model was identified as the finest ML algorithm for predicting SPI-3 and SPI-6 associated with other algorithms. This research demonstrates the Matern GPR model's efficacy in predicting multiscale SPI-3 and SPI-6 under climate variations. It can be helpful in soil and water resource conservation planning and management and understanding droughts in the entire basin areas of the country India. ? The Author(s) 2024. |
| author2 |
57193547008 |
| author_facet |
57193547008 Pande C.B. Sidek L.M. Varade A.M. Elkhrachy I. Radwan N. Tolche A.D. Elbeltagi A. |
| format |
Article |
| author |
Pande C.B. Sidek L.M. Varade A.M. Elkhrachy I. Radwan N. Tolche A.D. Elbeltagi A. |
| author_sort |
Pande C.B. |
| title |
Forecasting of meteorological drought using ensemble and machine learning models |
| title_short |
Forecasting of meteorological drought using ensemble and machine learning models |
| title_full |
Forecasting of meteorological drought using ensemble and machine learning models |
| title_fullStr |
Forecasting of meteorological drought using ensemble and machine learning models |
| title_full_unstemmed |
Forecasting of meteorological drought using ensemble and machine learning models |
| title_sort |
forecasting of meteorological drought using ensemble and machine learning models |
| publisher |
Springer |
| publishDate |
2025 |
| _version_ |
1825816266392207360 |
| score |
13.244109 |