Optimized LSTM based on improved whale algorithm for surface subsidence deformation prediction

In order to effectively control and predict the settlement deformation of the surrounding ground surface caused by deep foundation excavation, the deep foundation pit project of Baoding City Automobile Technology Industrial Park is explored as an example. The initial population approach of the whale algorithm (WOA) is optimized using Cubic mapping, while the weights of the shrinkage envelope mechanism are adjusted to avoid the algorithm falling into local minima, the improved whale algorithm (IWOA) is proposed. Meanwhile, 10 benchmark test functions are selected to simulate the performance of IWOA, and the advantages of IWOA in learning efficiency and convergence speed are verified. The IWOA-LSTM deep foundation excavation deformation prediction model is established by optimizing the input weights and hidden layer thresholds in the deep long short-term memory (LSTM) neural network using the improved whale algorithm. The IWOA-LSTM prediction model is compared with LSTM, WOA-optimized LSTM (WOA-LSTM) and traditional machine learning, the results show that the final prediction score of the IWOA-LSTM prediction model is higher than the score of other models, and the prediction accuracy is better than that of traditional machine learning.