Machine Learning for Humanitarian Aid Distribution and Crisis Management

Abstract

The application of machine learning (ML) in humanitarian aid distribution and crisis management has proven to be transformative in addressing the inefficiencies of traditional methods. Through predictive analytics, optimization algorithms, and real-time data processing, ML enables the efficient allocation of resources, better decision-making during disasters, and improved overall response efforts. This article explores the integration of ML into humanitarian aid systems, highlighting its potential in improving delivery times, optimizing resource usage, and enhancing crisis management efforts in diverse settings.