Research on a hybrid model for flood probability prediction based on time convolutional network and particle swarm optimization algorithm

Abstract

Accurate advance flood forecasting is beneficial for planning watershed flood prevention measures in advance. In this study, the PSO-TCN-Bootstrap flood forecasting model for the Tailan River Basin in Xinjiang was constructed by coupling particle swarm optimization algorithm (PSO), temporal convolutional neural network algorithm (TCN), and Bootstrap probability sampling algorithm. The model was tested based on 50 historical flood events from 1960 to 2014 using measured rainfall-runoff data in the Tailan River Basin. The results showed that under the same lead time conditions, the PSO-TCN-Bootstrap model had higher Nash efficiency coefficient, lower root mean square error and relative peak error in flood process forecasting. The PSO-TCN-Bootstrap flood forecasting model has better applicability and robustness in the Tailan River Basin. However, when the lead time exceeds 5h, the relative peak error in the PSO-TCN-Bootstrap model's flood forecasting will still exceed 20%. In the future, it is expected to integrate the mechanism of flood process occurrence and further improve the generalization ability of machine learning models in flood forecasting applications. The research results can provide a scientific basis for flood management in the Tailan River Basin.