Land Subsidence Prediction Model Based on the Long Short-Term Memory Neural Network Optimized Using the Sparrow Search Algorithm

Abstract

Land subsidence is a prevalent geological issue that poses significant challenges to construction projects. Consequently, the accurate prediction of land subsidence has emerged as a focal point of research among scholars and experts. Traditional mathematical models exhibited certain limitations in forecasting the extent of land subsidence. To address this issue, the sparrow search algorithm (SSA) was introduced to optimize the efficacy of the long short-term memory (LSTM) neural network in land subsidence prediction. This prediction model has been successfully applied to the Huanglong Commercial City project in the Guanghua unit of Wenzhou city, Zhejiang province, China, and has been compared with the predictions of other models. Using monitoring location 1 as a reference, the MAE, MSE, and RMSE of the test samples for the LSTM neural network optimized using the SSA are 0.0184, 0.0004, and 0.0207, respectively, demonstrating a commendable predictive performance. This new model provides a fresh strategy for the land subsidence prediction of the project and offers new insights for further research on combined models.