Influence on the Ecological Environment of the Groundwater Level Changes Based on Deep Learning

In recent years, frequent floods caused by heavy rainfall and persistent precipitation have greatly affected changes in groundwater levels. This has not only caused huge economic losses and human casualties, but also had a significant impact on the ecological environment. The aim of this study is to explore the effectiveness of the new method based on Long Short-Term Memory networks (LSTM) and its optimization model in groundwater level prediction compared with the traditional method, to evaluate the prediction accuracy of the different models, and to identify the main factors affecting the changes in groundwater level. Taking Chaoyang City in Liaoning Province as an example, four assessment indicators, R2, MAE, RMSE, and MAPE, were used. The results of this study show that the optimized LSTM model outperforms both the traditional method and the underlying LSTM model in all assessment metrics, with the GWO-LSTM model performing the best. It was also found that high water-table anomalies are mainly caused by heavy rainfall or heavy storms. Changes in the water table can negatively affect the ecological environment such as vegetation growth, soil salinization, and geological hazards. The accurate prediction of groundwater levels is of significant scientific importance for the development of sustainable cities and communities, as well as the good health and well-being of human beings.