基于GMM聚类的食品可及性模式深度探索及食品需求行为预测模型
GMM Clustering for In-depth Food Accessibility Pattern Exploration and Prediction Model of Food Demand Behavior
摘要：
了解粮食银行因粮食不安全而产生的需求动态对于优化运营成本和公平分配粮食至关重要，尤其是在需求不确定的情况下。因此，高斯混合模型（GMM）聚类选择提取模式。新颖之处在于，GMM聚类被应用于确定特定区域粮食不安全的可能原因，了解特定区域粮食援助网络的特点和结构，并进一步利用聚类结果探讨了不确定性食品需求行为的模式及其在库存管理和剩余食品再分配中的重要性，从而建立了一个两阶段的阶段混合食物需求估计模型从食品银行网络在俄亥俄州克利夫兰市获得的数据，和集群开发的研究和可视化。结果表明，该框架能够对食物可及性和援助模式进行深入识别，并利用GMM聚类结果，提高了统计和机器学习算法的预测精度。此外，根据不同层次的规划实施拟议的案例研究框架，取得了实际成果，使各规划小组感到非常轻松和舒适。
英文摘要：
Understanding the dynamics of food banks' demand from food insecurity is essential in optimizing operational costs and equitable distribution of food, especially when demand is uncertain. Hence, Gaussian Mixture Model (GMM) clustering is selected to extract patterns. The novelty is that GMM clustering is applied to identify the possible causes of food insecurity in a given region, understanding the characteristics and structure of the food assistance network in a particular region, and the clustering result is further utilized to explore the patterns of uncertain food demand behavior and its significant importance in inventory management and redistribution of surplus food thereby developing a two-stage hybrid food demand estimation model. Data obtained from a food bank network in Cleveland, Ohio, is used, and the clusters developed are studied and visualized. The results reveal that this proposed framework can make an in-depth identification of food accessibility and assistance patterns and provides better prediction accuracies of the leveraged statistical and machine learning algorithms by utilizing the GMM clustering results. Also, implementing the proposed framework for case studies based on different levels of planning led to practical results with remarkable ease and comfort intended for the respective planning team.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
