  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.
