Time Series Prediction for Food sustainability

TL;DR

This paper presents a machine learning-based statistical regression model to forecast future food resource shortages across countries, aiding sustainable development and resource management.

Contribution

It introduces a novel predictive system using regression models to identify potential food shortages, enhancing resource planning for sustainability.

Findings

Low absolute and root mean square errors demonstrate high prediction accuracy.

The model effectively forecasts top products at risk of shortage in different countries.

Results support its use for strategic planning in food resource management.

Abstract

With exponential growth in the human population, it is vital to conserve natural resources without compromising on producing enough food to feed everyone. Doing so can improve people's livelihoods, health, and ecosystems for the present and future generations. Sustainable development, a paradigm of the United Nations, is rooted in food, crop, livestock, forest, population, and even the emission of gases. By understanding the overall usage of natural resources in different countries in the past, it is possible to forecast the demand in each country. The proposed solution consists of implementing a machine learning system using a statistical regression model that can predict the top k products that would endure a shortage in each country in a specific period in the future. The prediction performance in terms of absolute error and root mean square error show promising results due to its low errors. This solution could help organizations and manufacturers understand the productivity and sustainability needed to satisfy the global demand.