The hazard source identification and risk assessment algorithm for the Yellow River based on the transformer model

With the increasing demand for efficient and accurate hazard source identification and risk assessment in water conservancy safety management, traditional risk management methods have gradually revealed limitations, including low efficiency and insufficient accuracy. This paper proposes a hazard source identification and risk assessment algorithm based on the Transformer model, aiming to automate the detection of hazards in water conservancy safety production and conduct precise risk evaluation and classification through multimodal data fusion. Using safety production data from the Shandong Yellow River Basin, the effectiveness of the proposed algorithm in hazard source identification and risk prediction was validated. The experimental results show that the Transformer-based method offers significant advantages in large-scale data processing, hazard source identification accuracy, and the timeliness of risk assessments.