Treffer: Integrating Stochastic Programming and Machine Learning for Enhanced Pre-disaster Relocation Planning

This paper proposes a novel framework that integrates stochastic programming and machine learning to optimize pre-disaster relocation strategies. Building upon existing game-theoretic and decision analysis models, this study presents a two-stage stochastic programming model coupled with predictive analytics to manage uncertainties associated with flooding risks and resident relocation behaviors. Machine learning algorithms, such as decision trees and gradient boosting, are employed to capture the variability in residents' decision-making, enhancing the precision of subsidy and policy impact forecasts. This combined approach offers governments innovative tools for implementing cost-effective, proactive relocation measures that mitigate long-term social and economic disruption. Additionally, by leveraging stochastic programming's robust handling of uncertainty and machine learning's data-driven insights, the framework ensures that relocation policies are both adaptive and equitable, addressing diverse community needs and long-term resilience planning.