Forecasting Climate-Driven Healthcare Demand in Agricultural Regions: A Multi-Modal AI Approach - Prince Tiwari; Rani Singh; Padma Mishra (International Journal of Climate Conditions, 2026)
The rapidly increasing instability of world climatic regimes has made past meteorological thresholds irrelevant, especially in the agricultural areas where monetary stability and well-being of humans are closely intertwined with an environmental situation. The more the frequency of 1 in every 1000-year events, i.e., heatwaves and catastrophic flooding increase, the greater the rural healthcare systems are in crisis, i.e., unable to predict a surge in demand because of data scarcity, and unable to maintain an infrastructural level to accommodate it. This research report is a detailed and architectural design of AI-assisted healthcare demand prediction specific to such scalar geographies. Our synthetic data generation Federated Learning (FL) combined with Generative Adversarial Networks (GANs) along with recent developments in rural area healthcare planning hybrid hydrological models will result in a disaster- resistant rural healthcare planning protocol. We use cross-sectional analyses on the multifaceted causal cascades among heat stress indices, agricultural decrease in yield (measured by NDVI) and individual hospital admissions spikes, such as Chronic Kidney Disease of unknown etiology (CKDu) and acute mental health crises. Moreover, we show the application of extreme event situation simulation with virtual hydrolabs and digital twins can be used to stress test hospital capacity. The results recommend the paradigm shift of site- specific, history-based predictions to regional, aggregated AI designs that consider environmental, epidemiology, and socio-economic factors in ensuring resilient healthcare provision despite the uncertainty of climate conditions.
Keywords: Climate change; Federated learning; Mental health; agricultural vulnerability; artificial intelligence for social good; chronic kidney disease of unknown etiology (CKDu); floods; healthcare demand forecasting; heatwaves; hybrid AI–physics models; normalized difference vegetation index (NDVI); rural healthcare systems; synthetic data generation; time-series forecasting