Predicting Food Donations with Adaptive AI

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Author: Denis Avetisyan

A new framework leverages reinforcement learning to improve the accuracy of food donation forecasts, even as demand and supply shift.

Even the most carefully constructed embodied force field (EFB) will ultimately succumb to the unpredictable pressures of real-world interaction.
Even the most carefully constructed embodied force field (EFB) will ultimately succumb to the unpredictable pressures of real-world interaction.

This research introduces FoodRL, a metalearning ensemble framework for time series forecasting of in-kind food donations that addresses challenges posed by concept drift and data volatility in food insecurity contexts.

Accurate forecasting is critical for effective resource allocation, yet traditional methods struggle with the inherent volatility of in-kind food donations. This paper introduces ‘FoodRL: A Reinforcement Learning Ensembling Framework For In-Kind Food Donation Forecasting’, a novel metalearning framework leveraging reinforcement learning to dynamically weight diverse forecasting models based on recent performance and contextual factors. Results demonstrate that FoodRL consistently outperforms baseline approaches—particularly during periods of disruption—and could facilitate the redistribution of food equivalent to 1.7 million additional meals annually. Could this adaptive ensemble approach unlock more resilient and impactful supply chain management within humanitarian aid networks?

Predictive Sandcastles: The Illusion of Food Bank Forecasting

Food banks, including organizations like Eastern and Western Food Banks (EFB and WFB), are critical in combating food insecurity, yet accurately forecasting donation volumes remains a significant operational challenge. Effective inventory management depends on anticipating incoming supplies, and current difficulties lead to logistical inefficiencies and waste. Traditional time-series forecasting methods – Exponential Smoothing (ETS), ARIMA, and Moving Averages – often prove ineffective due to the non-linear dynamics and volatility of charitable giving. External events, like Hurricanes Chris and Florence, further complicate matters, causing sudden donation spikes that disrupt established patterns.

Drifting Baselines: Recognizing When the Forecasts Lie

Donation patterns are subject to ‘concept drift’—shifts in underlying data distributions impacting predictive model performance. These shifts arise from macroeconomic conditions, seasonal behaviors, and global events. Ignoring concept drift leads to inaccurate forecasts and inefficient resource allocation. K-Means Clustering provides a valuable tool for identifying recurring patterns of concept drift within donation data. By grouping donations based on shared characteristics—amount, frequency, donor demographics—the algorithm reveals temporal changes. Significant shifts in cluster composition signal concept drift, allowing for model retraining and adaptation, ensuring predictions remain relevant and reliable.

FoodRL: A Reinforcement Learning Patch for Broken Predictions

FoodRL presents a meta-learning framework combining multiple forecasts using reinforcement learning. The system learns an optimal policy for ensembling predictions, maximizing accuracy across diverse tasks, adapting model weighting based on real-time performance and data characteristics. The framework incorporates time series feature extraction using the TSFEL library and was trained and evaluated using data from EFB and WFB. Proximal Policy Optimization (PPO) facilitates stable policy learning. Rigorous evaluation, utilizing Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE), demonstrates FoodRL’s effectiveness, achieving a MAPE of 9.77 on EFB and 13.19 on WFB, consistently outperforming comparative models and demonstrating statistical significance (p < 0.05).

A Temporary Reprieve: Scaling Resilience, Not Solving the Problem

FoodRL improves forecasting accuracy for food bank resource allocation, addressing the challenges of unpredictable donation patterns and fluctuating demand. Evaluations using data from EFB and WFB demonstrate a substantial increase in predicted meal provision, estimating an additional 1.66 million meals annually (monthly data) or 593,000 meals per year (weekly data), resulting from enhanced forecast precision and optimized inventory management. The framework’s ability to handle concept drift is critical for long-term planning and proactive resource allocation. While tested with EFB and WFB, the modular design facilitates seamless integration with other food banks and humanitarian organizations, offering not a solution, but a temporary reprieve from the inevitable chaos of supply and demand.

The pursuit of perpetually accurate forecasting, as evidenced by FoodRL’s adaptive ensembling, feels… optimistic. It’s a clever attempt to tame the chaos of donation patterns, acknowledging the inevitable concept drift. One could almost admire the effort, were it not so reminiscent of building sandcastles against the tide. As Carl Friedrich Gauss observed, “If you don’t know where you are going, any road will get you there.” This feels profoundly true; FoodRL strives for a specific destination (accurate forecasts) despite operating in a fundamentally unpredictable environment. The system will inevitably encounter data it wasn’t trained on, requiring constant adaptation. It’s not a failure of the framework, merely an admission that even the most elegant models are, at their core, elaborate notes left for future digital archaeologists trying to decipher why the donations suddenly plummeted.

What’s Next?

The presented framework, while demonstrating adaptive capabilities in forecasting food donations, merely shifts the locus of the problem. The core challenge isn’t improved prediction algorithms, but the inherent unpredictability of altruism and systemic inequities. Each successful iteration of forecast adaptation will inevitably encounter novel failure modes—new forms of disruption in supply chains, shifting demographics of need, or simply the exhaustion of donor goodwill. The algorithm will learn to chase a moving target, forever.

Further refinement of the reinforcement learning components feels, at best, like polishing the brass on the Titanic. The field seems fixated on optimizing for accuracy, yet ignores the fact that even a perfect forecast cannot manufacture food. The pursuit of more sophisticated ensemble methods should be tempered by acknowledging the limits of prediction when dealing with complex socio-economic systems. A more fruitful avenue may lie in integrating these models with robust resource allocation strategies that prioritize equity over efficiency.

Ultimately, the problem isn’t a lack of algorithms—it’s a surplus of assumptions. The focus shouldn’t be on anticipating need, but on building resilient systems that minimize it. The next iteration won’t be a better model; it will be a difficult realization that the true bottleneck isn’t data, but fundamental structural change. The question isn’t “can it predict?” but “what are we doing with the prediction?”

Original article: https://arxiv.org/pdf/2511.04865.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2025-11-11 00:55