AI System Boosts Dam Safety Alerts With Machine Learning Technique

To enhance the quality of automated recommendation generation in dam safety management, this study proposes a novel framework that integrates Monte Carlo Tree Search (MCTS) into the recommendation process. The method first applies Boolean filtering to the target hazard, based on four key attributes: sub-item category, evaluation factor, hazard type, and dam type. Next, semantic retrieval is conducted using cosine similarity between text embeddings. The retrieved results are then re-ranked using a weighted scheme that considers dam-type consistency, completeness of regulatory opinions, and semantic distance. This step constructs a highly relevant knowledge context for generation. Based on this context, a large language model (LLM) generates initial mitigation suggestions. To further refine these suggestions, MCTS is introduced to explore a space of edit actions—namely additions, deletions, and modifications—derived from historical cases. The exploration process is guided by the LLM’s self-evaluation scores. Experimental results demonstrate that the hybrid retrieval strategy increases NDCG@5 to 0.94. In a four-dimensional evaluation—covering relevance, coverage, feasibility, and clarity—retrieval-augmented generation outperformed pure LLM-based generation by an average of 0.65 points on a 5-point Likert scale. When enhanced with MCTS, the overall score further improved to 4.40. Convergence analysis shows that the MCTS process stabilizes the average reward within two tree depths and five search iterations, effectively balancing quality improvement and computational latency. In conclusion, the proposed method significantly enhances the completeness and engineering consistency of hazard mitigation recommendations. It provides an efficient and practical pathway for intelligent decision-making in hydraulic risk management. Moreover, it offers broader insights into interactive AI optimization for safety-critical infrastructure applications.