Abstract:
In modern times, the risk of food insecurity is a concern for policymakers at the global
and national levels, as the issue of hunger and malnutrition still exists. Food security is vulnerable
to any crises. The main goal of this paper is to create a neural-network-based predictive model to
forecast food consumption trends in Kazakhstan, aiming to reduce the risk of food insecurity. The
initial phase of this study involved identifying socioeconomic factors that significantly influence
food consumption behaviors in Kazakhstan. Principal component analysis was used to identify key
variables, which became the basis for modelling artificial neural networks. It was revealed that the
poverty rate, GDP per capita, and food price index are pivotal determinants of food consumption in
Kazakhstan. Two models were prepared: to predict food consumption on a national scale per capita
per month, and to predict the percentage distribution of various food categories. The prediction
of the percentage distribution of various food categories in Kazakhstan demonstrates the positive
modelling quality indicators and strengthens the assumption that network modelling can be used.
Predictions for total food consumption over the next three years indicate declining metrics, raising
concerns about the potential food insecurity risk in Kazakhstan.
