A multi-variable groundwater level prediction model based on PatchTST

Abstract

[Objective] Accurate and rapid prediction of dynamic groundwater level changes is crucial for scientific groundwater management, yet long-term predictions influenced by multiple factors remain insufficiently researched. [Methods] To enhance the capability of long-term groundwater level forecasting, a multivariate long-term prediction model based on PatchTST(PatchTST-GWL) was developed. This model utilized cross-correlation functions to analyze the multivariate correlations and lag effects among factors like groundwater extraction, surface water recharge, rainfall, and lateral recharge. Long-term forecasts were conducted on groundwater levels in four typical shallow monitoring wells in the western suburbs of Beijing. The model performance and accuracy were assessed using the Nash-Sutcliffe Efficiency(NSE), Root Mean Square Error(RMSE), and Mean Absolute Error(MAE). The model's interpretability was also analyzed using the controlled variable method. [Results] The result showed that the prediction accuracy of the PatchTST-GWL model improves with the extension of the forecasting period. For 90 and 180 days forecasting periods, the NSE coefficients of groundwater level simulations exceeded 0.9 across all monitoring wells, with MAE, MSE, and RMSE reductions of 10% to 80% compared to commonly used deep learning models like Attention-Bi-LSTM and SVM. [Conclusion] The PatchTST-GWL model exhibits a significant advantage in the performance of long-term groundwater level predictions. By incorporating cross-correlation functions to calculate the lag effects of groundwater extraction, rainfall, surface water recharge, and lateral recharge changes on groundwater levels, the model significantly enhances prediction accuracy. Furthermore, the predictions align with the response patterns of various influencing factors, consistent with objective physical laws, demonstrating good interpretability. This model can accurately and swiftly predict groundwater levels, effectively supporting scientific assessments and rational utilization of groundwater resources.

Keywords

groundwater level prediction / Transformer / multi-head attention / rainfall / human activity / PatchTST-GWL / artificial intelligence / medium and long term forecast