
International Journal For Multidisciplinary Research
E-ISSN: 2582-2160
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A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal
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Volume 7 Issue 3
May-June 2025
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Intelligent Rainfall Prediction Using Advanced AI Models
Author(s) | Prof. Pratima Tiwari |
---|---|
Country | India |
Abstract | This research deals with question of estimating rainfall in the Indian region is a vital task in order to sustain agriculture and the environment. As a more sophisticated type of deep learning technology, the present research applied Recurrent Neural Networks (RNN) to improve the existing approaches for the rainfall forecasting. First, to create RNN models that could forecast the next-day rainfall, this study used a broad Rainfall Dataset which set an 84% accuracy baseline defined in prior research. Cutting down the measurement loss and following the objective to enhance the predictive potential, this study supplemented previous approach with the ensemble methods and employed the Voting Classifier method in particular. This efficient methodology entailed the usage of numerous points for which multiple models produced to enhance a better prediction platform. The results were quite encouraging; they showed that the ensemble approach improve the accuracy of the model to an exceptional level of 100% precision. In addition to the technical innovation, this research offers great benefits by proving the applicability of highly developed machine learning algorithms for the agricultural forecasting. Besides demonstrating the capability of RNN and ensemble approaches in the present study, the work also presents important implications for agricultural decision making, crop yield prediction, and insurance risk modeling across climate change vulnerable zones. |
Keywords | Rainfall, Prediction, RNN, DL, ML, Voting Classifier |
Field | Computer > Artificial Intelligence / Simulation / Virtual Reality |
Published In | Volume 7, Issue 3, May-June 2025 |
Published On | 2025-06-01 |
DOI | https://doi.org/10.36948/ijfmr.2025.v07i03.46578 |
Short DOI | https://doi.org/g9m2d7 |
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E-ISSN 2582-2160

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