关键词: 溃坝, 洪峰流量, 土石坝, 耳廓狐算法, 广义回归神经网络

Abstract:

The accuracy of predicting the peak flood flow at the breach of earth-rock dam is crucial for dam break analysis. To improve the prediction accuracy of the post-breach peak flood flow, this paper presents a prediction model based on the General Regression Neural Network (GRNN), optimized by the Fennec Fox Optimization (FFA) algorithm for hyperparameters, to forecast the peak flood flow caused by dam breaches. Using a database of domestic and international dam failure cases, the model selects three factors as input variables: the reservoir capacity above the breach bottom, the water depth above the breach bottom, and the breach depth, to build the FFA-GRNN prediction model. To evaluate the model’s precision and fitting accuracy in predicting peak flood discharge at dam break, we compared it with four other intelligent algorithms. Results show that the proposed FFA-GRNN model has a lower Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and a higher coefficient of determination (R²) than other models, indicating superior computational precision and fitting performance.

Key words: dam break, peak discharge, earth-rock dam, Fennec Fox Algorithm, Generalized Regression Neural Network