Coupled ALO-LSTM and feature attention mechanism prediction model for seepage pressure of earth-rock dam

The existing prediction model of seepage pressure of earth-rock dam lacks quantitative analysis of the influence degree of each influencing factor in seepage effect quantity, which makes it difficult to explore the internal influencing mechanism of seepage effect quantity change. In view of the above problems, this paper proposes a prediction model of seepage pressure of earth-rock dam by coupling ALO-LSTM and feature attention mechanism from the perspectives of time dimension and characteristic dimension. The model firstly adopts principal component analysis to reduce dimension of each influencing factor. Then, a long short-term memory network based on ant lion optimization algorithm is proposed to optimize the number of neurons in long short-term memory network by using random walk in ant lion optimization and roulette to capture the deep information of osmotic pressure data in time dimension. Furthermore, the feature attention mechanism is introduced in the feature dimension to calculate the contribution rate of each influencing factor to the osmotic effect volume, so as to find out the internal reasons for the change of the seepage effect volume adaptively. The case analysis shows that the proposed model has high accuracy, and its MAPE, RMSE and MAE on test samples are 0.28%, 0.022m and 0.17m, respectively. In addition, the contribution rate of water level component to osmotic effect is 47.9%, followed by precipitation temperature and aging component, which are 33.5%, 9.8% and 8.8%, respectively. © 2022, China Water Power Press. All right reserved.