Modeling the Climate-Conflict-Migration Interplay in Sudan by Integrating GAMS and Spatio-Temporal Neural Networks

The ongoing humanitarian crises in Sudan, which are being exacerbated by escalating climate change, persistent conflict, and migratory waves, highlight the critical need for predictive, data-driven models to guide efficient response plans. In order to investigate and predict the intricate links between climate variability, conflict intensity, and migration patterns in Sudan at the subnational level, this study combines Spatio-Temporal Neural Networks (STNNs) with Generalized Additive Models (GAMs). The hybrid modeling framework provides strong insights into displacement patterns and conflict dynamics by capturing both spatial-temporal dependencies and nonlinear effects. GAMs showed a high positive correlation between conflict severity and precipitation levels, as well as statistically significant nonlinear relationships between food prices and relocation. In the meantime, the STNNs performed better than traditional modeling techniques, with R2 values of 0.89 and 0.84 for conflict intensity and regional displacement prediction, respectively. These excellent performance measures show how well the model captures real-world dynamics and provide a useful tool for humanitarian predictions. Prototype app for visualizing migration and conflict forecasts are included in the study, but they are still in the early stages of development. Future studies aim to improve operational utility and decision-making support in the field through real-time application. The results highlight how crucial it is for humanitarian research to combine machine learning and statistical modeling. This study offers practical insights that can enhance early warning systems, policymaking, and disaster preparedness in climate-vulnerable areas like Sudan by identifying important drivers of displacement and violence.