Improving Food Demand Forecasting through Stacked Regression and Hybrid Machine Learning Methods
Keywords:
Machine Learning, Food Demand Prediction, Stacking Regression, Root Mean Squared Error, Mean Absolute ErrorAbstract
Food demand forecasting is crucial in modern disaster management, particularly amid challenges magnified by the COVID-19 pandemic. The pandemic emphasized the need for accurate forecasting, especially with increased food deliveries and demand surges. Proactive measures are essential to mitigate potential food shortages and prevent losses. Integrating blockchain and Machine Learning (ML) algorithms enhance forecasting accuracy. This study evaluates the efficacy of different ML algorithms - in food demand forecasting, utilizing a monthly ration consumption dataset of 20 districts of Jammu & Kashmir UT. Data pre-processing techniques, including handling missing values, outlier removal, and data normalization, were employed. Additionally, feature selection approaches identified the most relevant predictors for demand forecasting. Our aim is to create a predictive model that achieves high accuracy and efficiency through the integration of innovative machine learning methods and hybridizing current methodologies. Our tests result show that Stacking Regression, which combines three different algorithms (Random Forest, Support Vector Regression, and Ridge), performs better than other methods at predicting outcomes with a lower Root Mean Squared Error (RMSE) score, which indicates how close our predictions are to the actual values. After Hyperparameter tuning, the best RMSE score obtained was 0.11252 using the Ridge model. However, the Stacking Regression model resulted the best RMSE score of 0.10945. This shows that Stacking Regression emerges as the optimal algorithm for demand forecasting within JkBFMs: Blockchain food supply chain management systems, offering simplicity, interpretability, and efficiency in decision-making.
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