2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 19 , Issue 2 , PP: 288-303, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Sachin Subhashrao Patil 1 , Sonali Ridhorkar 2 *
Doi: https://doi.org/10.54216/FPA.190221
Predicting rainfall proves critical for businesses to organize their water resources, make agricultural choices, and prevent disasters. Therefore, proposed model presents a novel approach, namely Heuristic Intelligence towards Enhancing Rainfall Prediction with Artificial Neural Networks (HI2NN) to enhance rainfall prediction by designing heuristic Intelligence combined with Improved Artificial Neural Networks (IANNs). The proposed HI2NN framework leverages heuristic optimization techniques to fine-tune ANN parameters to improve prediction accuracy. Prediction accuracy is computed through our designed custom accuracy metric. The methodology uses historical weather information to extract complex non-linear patterns, which neural models generate from the designed big dataset. The accuracy level of rainfall predictions using our methodology achieves 92%, which demonstrates superior performance than traditional approaches that include random forest and decision tree and XGBoost models. The new forecasting systems develop higher reliability through collaborative efforts between heuristic algorithms and neural networks as described in this research work targeting challenging meteorological forecasts.
Correlation , Meteorological Data , Machine Learning , Deep Learning , Rainfall , Prediction , Heuristics
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