Efficacy of machine learning in simulating precipitation and its extremes over the capital cities in North Indian states
Abstract Climate change-induced precipitation extremes are a pressing global concern. This study investigates the predictability of precipitation patterns and extremes across North Indian states from 1984 to 2023 using NASA’s Prediction of Worldwide Energy Resources (POWER) datasets and machine lear...
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Nature Portfolio
2025-03-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-84360-w |
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| author | Aayushi Tandon Amit Awasthi Kanhu Charan Pattnayak |
| author_facet | Aayushi Tandon Amit Awasthi Kanhu Charan Pattnayak |
| author_sort | Aayushi Tandon |
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| description | Abstract Climate change-induced precipitation extremes are a pressing global concern. This study investigates the predictability of precipitation patterns and extremes across North Indian states from 1984 to 2023 using NASA’s Prediction of Worldwide Energy Resources (POWER) datasets and machine learning (ML) models. The current ML model builds on the relationship between rainfall and key climatic parameters such as dew point temperature and relative humidity, showing a strong positive correlation (CC = 0.4) significant at the 0.05 level. In simulating precipitation, Random Forest Classifier (RFC) achieved the highest accuracy (~ 83%) for Rajasthan and Uttar Pradesh, while Support Vector Classifier (SVC) performed best (79–83% accuracy) in other states. However, ML models exhibited approximately 5% lower skill in higher elevated stations as compared to lower ones, due to differing atmospheric mechanisms. For extreme precipitation events (10th and 95th percentiles of intensity), RFC consistently outperformed SVC across all states showing superior ability to distinguish extreme from non-extreme events (Area Under Curve ~ 0.90) and better model calibration (Brier Scores ~ 0.01). The developed ML models effectively simulated precipitation and extreme patterns, with RFC excelling at classifying extreme events. These findings can aid disaster preparedness and water resource management in regions with varied topography and complex terrain. |
| format | Article |
| id | doaj-art-deaaa7e2cfe34d22b41ee9f21ad638c1 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-deaaa7e2cfe34d22b41ee9f21ad638c12025-08-20T03:40:49ZengNature PortfolioScientific Reports2045-23222025-03-0115111810.1038/s41598-024-84360-wEfficacy of machine learning in simulating precipitation and its extremes over the capital cities in North Indian statesAayushi Tandon0Amit Awasthi1Kanhu Charan Pattnayak2Department of Applied Sciences, University of Petroleum & Energy StudiesDepartment of Applied Sciences, University of Petroleum & Energy StudiesSchool of Earth and Environment, University of LeedsAbstract Climate change-induced precipitation extremes are a pressing global concern. This study investigates the predictability of precipitation patterns and extremes across North Indian states from 1984 to 2023 using NASA’s Prediction of Worldwide Energy Resources (POWER) datasets and machine learning (ML) models. The current ML model builds on the relationship between rainfall and key climatic parameters such as dew point temperature and relative humidity, showing a strong positive correlation (CC = 0.4) significant at the 0.05 level. In simulating precipitation, Random Forest Classifier (RFC) achieved the highest accuracy (~ 83%) for Rajasthan and Uttar Pradesh, while Support Vector Classifier (SVC) performed best (79–83% accuracy) in other states. However, ML models exhibited approximately 5% lower skill in higher elevated stations as compared to lower ones, due to differing atmospheric mechanisms. For extreme precipitation events (10th and 95th percentiles of intensity), RFC consistently outperformed SVC across all states showing superior ability to distinguish extreme from non-extreme events (Area Under Curve ~ 0.90) and better model calibration (Brier Scores ~ 0.01). The developed ML models effectively simulated precipitation and extreme patterns, with RFC excelling at classifying extreme events. These findings can aid disaster preparedness and water resource management in regions with varied topography and complex terrain.https://doi.org/10.1038/s41598-024-84360-wClimate changeMachine learningNorth Indian statesPrecipitation patternsRandom forestSupport vector machine |
| spellingShingle | Aayushi Tandon Amit Awasthi Kanhu Charan Pattnayak Efficacy of machine learning in simulating precipitation and its extremes over the capital cities in North Indian states Scientific Reports Climate change Machine learning North Indian states Precipitation patterns Random forest Support vector machine |
| title | Efficacy of machine learning in simulating precipitation and its extremes over the capital cities in North Indian states |
| title_full | Efficacy of machine learning in simulating precipitation and its extremes over the capital cities in North Indian states |
| title_fullStr | Efficacy of machine learning in simulating precipitation and its extremes over the capital cities in North Indian states |
| title_full_unstemmed | Efficacy of machine learning in simulating precipitation and its extremes over the capital cities in North Indian states |
| title_short | Efficacy of machine learning in simulating precipitation and its extremes over the capital cities in North Indian states |
| title_sort | efficacy of machine learning in simulating precipitation and its extremes over the capital cities in north indian states |
| topic | Climate change Machine learning North Indian states Precipitation patterns Random forest Support vector machine |
| url | https://doi.org/10.1038/s41598-024-84360-w |
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