Publication
Abstract
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.
Keywords
Stochastic Programming , Machine Learning , Pre-disaster Relocation , Flooding Risk Management , Resident Relocation Behavior , Decision Analysis , Predictive Analytics , Gradient Boosting , Decision Trees , Proactive Relocation Strategies .
Metadata
Disciplines: Computer Science
Subjects: Artificial Intelligence
Cite This Article
APA Style
Zhou, Y. & Cen, Z. (2025). Integrating stochastic programming and machine learning for enhanced pre-disaster relocation planning. Academic Journal of Natural Science, 2(1), 7-11. https://doi.org/10.70393/616a6e73.323631
Acknowledgments
The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions.
FUNDING
INSTITUTIONAL REVIEW BOARD STATEMENT
DATA AVAILABILITY STATEMENT
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
INFORMED CONSENT STATEMENT
CONFLICT OF INTEREST
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
AUTHOR CONTRIBUTIONS
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