Examining the Impacts of Recent Water Availability on the Future Food Security Risks in Pakistan Using Machine Learning Approaches

Wilayat Shah; Junfei Chen; Irfan Ullah; Ashfaq Ahmad Shah; Bader Alhafi Alotaibi; Sidra Syed; Muhammad Haroon Shah

Examining the Impacts of Recent Water Availability on the Future Food Security Risks in Pakistan Using Machine Learning Approaches

2024

Food and water security are critical challenges in Pakistan, exacerbated by rapid population growth, climate variability, and limited resources. This study explores the application of machine learning techniques to address these issues. We specifically examine the dimensions of food and water security in Pakistan, employing data-driven methods to enhance crop yield predictions, food production forecasting, and water resource management. Using secondary data, we refine machine learning models, such as random forest and linear regression, to analyze water availability, crop yield, and crop production. These models aim to optimize resource distribution, improve irrigation efficiency, and minimize water waste. We propose developing AI-based predictions to address food and water crises proactively. Our findings indicate that food insecurity persists in Pakistan, worsened by uneven distribution. Given the country&rsquo:s high dependence on irrigation for crop production, we analyze the impact of population growth on food production and water demand. We recommend a comprehensive strategy that includes infrastructure development, improved water use efficiency in agriculture, and policy adjustments to balance food imports and exports.

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