ASSESSMENT AND PREDICTION MODEL FOR COMMUNITY RESILIENCE IN CHINA BASED ON GANS AND RANDOM FOREST REGRESSION
DOI:
https://doi.org/10.21837/pm.v23i38.1823Keywords:
Community Resilience, GANs, Random Forest Regression, Urbanization, Disaster PreventionAbstract
Community resilience is a vital aspect of urban planning and disaster management, especially in rapidly urbanizing countries like China, where frequent natural disasters pose significant threats. This study addresses the challenge of assessing and predicting community resilience in China by developing an innovative model using Generative Adversarial Networks (GANs) and Random Forest Regression. The model specifically tackles issues of data insufficiency and prediction inaccuracy in traditional methods. The study surveyed 33 diverse communities across various regions in China and generated 300 additional virtual samples using GANs to enhance model robustness. The model incorporates the Analytic Hierarchy Process (AHP) to ensure scientific accuracy in determining indicator weights. Results indicate a substantial improvement in predictive performance, with a Mean Squared Error (MSE) of 0.00055 and a coefficient of determination (R²) of 0.99578. This study contributes a novel framework for evaluating community resilience, providing a scientific foundation for disaster prevention and mitigation strategies in urban governance.
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