A Computational Structure for Anticipating Refugee Movements and Assessing Resource Demand

The rising occurrence of conflicts, political turmoil, and climate-induced disasters has turned refugee displacement into a pressing global issue, where conventional response mechanisms frequently fall short and are merely reactive. This research introduces a machine learning-driven Refugee Movement Prediction and Resource Planning System that leverages historical displacement data to anticipate future refugee numbers and assess the necessary resources, such as food, water, shelter, and medical supplies. The suggested method combines time-series forecasting through ensemble learning with simulation-based resource estimation to facilitate proactive humanitarian planning. By merging predictive analytics with a web-based visualization platform, the system empowers data-informed decision-making for governments and aid organizations. The findings highlight the capability of such frameworks to enhance preparedness, streamline resource distribution, and improve the effectiveness of humanitarian response strategies.

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