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@article{198782,
        author = {Akshay G N and Tanay Goel and Anand K and R Subhashini},
        title = {Rainfall-Aware Hybrid Ensemble Framework for Risk-Optimized Crop Decision Support},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {9990-9998},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=198782},
        abstract = {Agricultural decision-making has become more challenging due to climatic variability and market conditions, which have rendered traditional crop recommendation systems inadequate for agricultural planning. Most of the traditional crop recommendation systems have focused mainly on yield prediction, without taking into account the associated economic risks, historical production stability, or the associated prediction uncertainties. This paper proposes a rainfall-aware hybrid ensemble approach to an intelligent crop decision support system, which takes into account yield forecasting as well as associated economic risks. The proposed crop recommendation system uses a weighted hybrid ensemble of Random Forest, XGBoost, and LightGBM to predict the crop yield based on rainfall and historical production patterns. A time-based validation approach has been used to prevent temporal leakage, which is essential to render the proposed system practical. Additionally, the proposed system has used a Yield Stability Index, a Price Volatility Index, to quantify the associated historical production stability, as well as the associated market risks, which have been combined to compute the normalized Risk Score and the associated Risk-Adjusted Profit.},
        keywords = {Hybrid Ensemble Learning, Rainfall-Based Yield Prediction, Risk-Adjusted Profit, Crop Decision Support, Yield Stability Index, Price Volatility Modeling, Time-Based Validation.},
        month = {April},
        }