Study on Urban Flood Risk Assessment and Precipitation Prediction Based on SA-Informer Model

Abstract

This study focuses on urban flood risk assessment and precipitation prediction, constructs a technical framework centred on the SA-Informer model, and systematically elaborates the algorithmic innovation and application value of this model in precipitation prediction. Firstly, at the risk assessment level, this study integrates multi-source data indicators based on the Pressure-State-Response (P-S-R) model, and confirms that heavy precipitation, as a core driving factor, is significantly associated with the risk level jumping through the AHP-CRITIC combined weighting algorithm, the improved ternary coupling coordination degree model, and the stepwise regression analysis method, which identifies the most influential indicators affecting system coordination. This lays the foundation of causal logic for the construction of the prediction model. Further, the SA-Informer model is innovatively proposed, which combines the global search capability of the simulated annealing algorithm and the long time series processing advantage of the Informer model, and by dynamically adjusting the learning rate, input sequence length and other parameters, the nonlinear characteristics and seasonal patterns of precipitation data are effectively captured. Experiments show that the model significantly improves the prediction accuracy compared with the traditional time series prediction model, and provides highly reliable feed-forward information for flood warning. Based on data fusion and model algorithms, the framework provides a complete solution for urban flood prevention and control, and its methodology and model architecture are highly adaptable and scalable, which can be extended to other climate-sensitive cities in the fields of disaster prediction, risk management and sustainable development, and help to improve urban resilience and scientific decision-making capability.