A Machine Learning Framework for Predicting Restaurant Food Waste to Support Sustainable Food Management

• URL: http://arxiv.org/abs/2609.08078v1
• Date: Tue, 08 Sep 2026 00:43:53 GMT
• Title: A Machine Learning Framework for Predicting Restaurant Food Waste to Support Sustainable Food Management
• Abstract summary: Food waste in the restaurant sector poses a substantial challenge to environmental sustainability and economic efficiency.<n>This paper presents an exploratory machine learning framework for estimating daily restaurant food waste quantities from operational and contextual features.
• Score: 1.9573380763700712
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Food waste in the restaurant sector poses a substantial challenge to environmental sustainability and economic efficiency. This paper presents an exploratory machine learning framework for estimating daily restaurant food waste quantities from operational and contextual features. A structured dataset was constructed by integrating restaurant demand records, meteorological data and temporal event indicators, yielding 77,980 records across 27 features. Because large-scale ground-truth food waste measurements are not publicly available, the target variable was derived from operationally justified assumptions, with the complete construction formula and controlled stochastic variability disclosed for full reproducibility. Four supervised regression models, namely Linear Regression, Decision Tree, Random Forest and Gradient Boosting, were evaluated under a chronological 70-30 train-test split that respects the temporal ordering of restaurant operations, augmented by 5-fold time-series cross-validation. All reported metrics are explicitly scoped to performance against the constructed target and do not imply validation against measured food waste. Ensemble methods consistently outperformed linear baselines. Random Forest attained an MAE of 6.19 kg, RMSE of 8.36 kg and $R^2$ of 0.817 on the realistic feature subset following systematic exclusion of algebraically leakage-prone variables. Feature importance analysis identified menu diversity, operational area and temporal activity patterns as the primary predictive drivers. The full dataset, target construction formula, codebase and experimental configurations are publicly released to support reproducibility and future extension to empirically measured waste data.

Related papers

• Orthogonal JEPA: Factorized Predictive States for Latent World Models [95.20158869896393]
  We introduce method, a latent world-modeling framework based on predictive factorization.<n>We show that method can allocate redundant capacity to dominant signals while providing weak or conflicting gradients to less dominant predictive structure.<n>Experiments on controlled vision, single-cell transcriptomics, longitudinal health records, continuous control, and molecular dynamics evaluate representation quality, forecasting, planning, and long-horizon stability.
  arXiv  Detail & Related papers  (2026-08-20T13:59:57Z)
• TimeSage-EV: A Live Benchmark for Agentic Time Series Analysis in Evolving Environments [46.167956432673776]
  TimeSage-EV is a live benchmark for agentic time series analysis in evolving environments.<n>It tracks 60 real institutional scenarios across 6 domains, comprising 1,485 scenario-period QA pairs from Feb 2023 to May 2026.<n>TimeSage-EV evaluates state identification, data summarization, and outlook reasoning.
  arXiv  Detail & Related papers  (2026-08-14T12:53:21Z)
• Observationally Informed Adaptive Causal Experimental Design [55.998153710215654]
  We propose Active Residual Learning, a new paradigm that leverages the observational model as a foundational prior.<n>This approach shifts the experimental focus from learning target causal quantities from scratch to efficiently estimating the residuals required to correct observational bias.<n> Experiments on synthetic and semi-synthetic benchmarks demonstrate that R-Design significantly outperforms baselines.
  arXiv  Detail & Related papers  (2026-03-04T06:52:37Z)
• Revisiting Multivariate Time Series Forecasting with Missing Values [65.30332997607141]
  Missing values are common in real-world time series.<n>Current approaches have developed an imputation-then-prediction framework that uses imputation modules to fill in missing values, followed by forecasting on the imputed data.<n>This framework overlooks a critical issue: there is no ground truth for the missing values, making the imputation process susceptible to errors that can degrade prediction accuracy.<n>We introduce Consistency-Regularized Information Bottleneck (CRIB), a novel framework built on the Information Bottleneck principle.
  arXiv  Detail & Related papers  (2025-09-27T20:57:48Z)
• Artificial Intelligence in the Food Industry: Food Waste Estimation based on Computer Vision, a Brief Case Study in a University Dining Hall [1.864621482724548]
  This study presents a cost-effective computer vision framework that estimates plate-level food waste.<n>Four fully supervised models were trained using a capped dynamic inverse-frequency loss and AdamW metrics.<n>All models achieved satisfying performance, and for each food type, at least one model approached or surpassed 90% DPA.
  arXiv  Detail & Related papers  (2025-07-19T15:21:29Z)
• Food Delivery Time Prediction in Indian Cities Using Machine Learning Models [0.4893345190925178]
  This research addresses gaps by integrating real-time contextual variables into predictive models.<n>We systematically compare various machine learning algorithms, including Linear Regression, Decision Trees, Bagging, Random Forest, XGBoost, and LightGBM.<n> Experimental results demonstrate that the LightGBM model achieves superior predictive accuracy, with an R2 score of 0.76 and Mean Squared Error (MSE) of 20.59, outperforming traditional baseline approaches.
  arXiv  Detail & Related papers  (2025-03-19T13:02:23Z)
• Leveraging LLMs for Predictive Insights in Food Policy and Behavioral Interventions [1.979158763744267]
  Food consumption and production contribute significantly to global greenhouse gas emissions. Food policy initiatives have explored interventions to reshape production and consumption patterns. This paper demonstrates that a fine-tuned large language model (LLM) can accurately predict the direction of outcomes.
  arXiv  Detail & Related papers  (2024-11-13T12:21:13Z)
• Anticipatory Understanding of Resilient Agriculture to Climate [66.008020515555]
  We present a framework to better identify food security hotspots using a combination of remote sensing, deep learning, crop yield modeling, and causal modeling of the food distribution system. We focus our analysis on the wheat breadbasket of northern India, which supplies a large percentage of the world's population.
  arXiv  Detail & Related papers  (2024-11-07T22:29:05Z)
• RoDE: Linear Rectified Mixture of Diverse Experts for Food Large Multi-Modal Models [96.43285670458803]
  Uni-Food is a unified food dataset that comprises over 100,000 images with various food labels.<n>Uni-Food is designed to provide a more holistic approach to food data analysis.<n>We introduce a novel Linear Rectification Mixture of Diverse Experts (RoDE) approach to address the inherent challenges of food-related multitasking.
  arXiv  Detail & Related papers  (2024-07-17T16:49:34Z)
• Forecasting trends in food security with real time data [0.0]
  We present a quantitative methodology to forecast levels of food consumption for 60 consecutive days, at the sub-national level, in four countries: Mali, Nigeria, Syria, and Yemen. The methodology is built on publicly available data from the World Food Programme's global hunger monitoring system.
  arXiv  Detail & Related papers  (2023-12-01T14:42:37Z)
• Dish detection in food platters: A framework for automated diet logging and nutrition management [1.7855867849530096]
  Dish detection from food platters is a challenging problem due to a visually complex food layout. We present an end-to-end computational framework for diet management, from data compilation, annotation, and state-of-the-art model identification. We implement the framework in the context of Indian food platters known for their complex presentation.
  arXiv  Detail & Related papers  (2023-05-12T15:25:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.