Adapting Hybrid CBR–RBF Models for Casualty and Emergency Supply Demand Forecasting in High-Altitude Regions: A Case Study of the 2022 Luding Earthquake

Abstract Earthquakes frequently occur worldwide and cause extensive damage. Effective emergency response is crucial, and accurate forecasting of emergency supply needs is essential for efficient disaster relief operations. This study proposes an emergency supply demand prediction model for high-altitude regions by combining case-based reasoning (CBR) with a radial basis function (RBF) neural network. Building on previous research into factors influencing earthquake casualties, we identified six key parameters, namely, the magnitude, seismic intensity, seismic fortification intensity, affected population, population density, and the number of damaged houses. After evaluating various machine learning models, the RBF neural network was found to have the highest accuracy in predicting earthquake casualties. Then, the model was integrated with CBR to further estimate emergency supply demand, with regional adjustments made according to the geological structures and specific supply needs of the western Sichuan plateau. To enhance practical usability, a graphical user interface was developed, enabling users to input earthquake parameters and obtain predictions of casualties and supply requirements. This research provides a valuable decision-support tool for earthquake emergency response in high-altitude regions and offers methodological insights for emergency supply demand prediction in other complex environments.