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@article{188336,
        author = {Anjali Shelar and Ninad Shrimali and Siddhesh Pawar and Sharan Gujarathi},
        title = {Data science for meteorological applications},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {1768-1779},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=188336},
        abstract = {Accurate weather classification is crucial for enhancing meteorological forecasting, impacting agriculture, disaster management, and urban planning. Traditional statistical methods lack adaptability in handling complex, nonlinear weather patterns, while machine learning (ML) approaches provide more robust predictive capabilities. However, individual ML models struggle with feature dependencies and temporal variations in meteorological data. To address this, this study introduces an optimized weather classification framework leveraging ensemble learning. The proposed approach integrates feature engineering with a Random Forest (RF) classifier and an ensemble model to improve classification accuracy. The dataset undergoes preprocessing, including noise reduction, temporal feature extraction, and transformation into engineered features. The RF classifier is trained with hyperparameter tuning, and an ensemble model is constructed by blending top-performing classifiers, including XGBoost, LightGBM, and Gradient Boosting. Evaluated on historical meteorological datasets, the proposed ensemble model achieves a 10–15% improvement in accuracy, precision, and recall compared to standalone classifiers, demonstrating its effectiveness in enhancing weather classification reliability.},
        keywords = {Weather Classification, Machine Learning (ML), Random Forest (RF), Ensemble Learning, Feature Engineering, Meteorological Forecasting, Model Performance Evaluation, Precision-Recall Analysis.},
        month = {December},
        }