Development and Empirical Application of a Debris Flow Entrainment Rate Prediction Model Utilizing Generative AI

Debris flows represent a profound natural hazard, exerting devastating impacts on infrastructure, ecosystems, and human lives. During their downstream progression, debris flows transport a diverse range of materials, including soil, rocks, and vegetation, a phenomenon termed sediment entrainment. This entrainment process is governed by a complex interplay of geomorphological features, hydrological conditions, triggering factors, physical properties, and geological characteristics. Traditional predictive methods, which predominantly rely on empirical data and physically-based models, have shown limitations in capturing the variability and intricacy of environmental conditions. This study seeks to develop a sophisticated predictive model for debris flow sediment transport rates by leveraging advanced artificial intelligence (AI) techniques. The AI-driven approach enables efficient processing of extensive datasets and the recognition of nonlinear and intricate patterns, providing more rapid and precise predictions compared to conventional methodologies. The research framework comprises five distinct stages. First, critical factors influencing sediment transport rates were systematically identified and collected. Second, a comprehensive database was constructed, incorporating detailed data from 54 debris flow sites across South Korea. Third, data preprocessing was conducted, including correlation analysis and multicollinearity diagnostics to refine variable selection, followed by feature scaling and data augmentation utilizing generative AI techniques to enhance dataset robustness. Fourth, the dataset was partitioned into training and validation subsets, and various machine learning regression algorithms were employed to identify the optimal predictive model. Finally, the proposed model was empirically validated using a case study of the 2023 large-scale debris flow disaster in Yecheon County, Gyeongsangbuk-do, South Korea. The findings underscore the remarkable predictive precision and adaptability of the AI-based model, surpassing the performance of traditional physically-based approaches. This advancement holds significant potential for enhancing debris flow risk management and proactive mitigation strategies. Moreover, the study underscores the transformative role of AI technologies in addressing the challenges of predicting and managing complex natural hazards, offering a robust foundation for diverse applications in hazard mitigation and disaster resilience