4. Explainable ensemble learning for accurate hydroponic stock and sales prediction
DOI:
https://doi.org/10.61591/jslhu.26.1085Từ khóa:
linear regression, random forest, XGBoost;, Model-agnostic local explanation method;, Sustainable Development Goals;, Stock and Sales predicted.Tóm tắt
Growing global food demand calls for more sustainable agricultural practices. While hydroponic farming uses resources efficiently, predicting stock levels and sales remains difficult due to fluctuating conditions. This study proposes an optimized ensemble framework that integrates linear regression, random forest, and XGBoost, with weights refined through an evolutionary algorithm. Evaluated using root mean square error (RMSE) and mean absolute error (MAE), the model outperforms individual approaches, reducing RMSE by 43.82% for stock prediction and 55.3% for sales forecasting. A model-agnostic local explanation method is used to interpret the results, highlighting harvested crop volume and historical sales as key predictors. Future work will focus on integrating real-time data to improve adaptability and forecasting performance.
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