Construction of spatio-temporal coupling model for groundwater level prediction: a case study of Changwu area, Yangtze River Delta region of China

The dynamic monitoring data of groundwater level is an important basis for understanding the current situation of groundwater development and the utilization and planning of sustainable exploitation. The dynamic monitoring data of groundwater level are typical spatio-temporal sequence data, which have the characteristics of non-linearity and strong spatio-temporal correlation. The trend of dynamic change of groundwater level is the key factor for the optimal allocation of groundwater resources. However, most of the existing groundwater level prediction models are insufficient in considering the temporal and spatial factors and their spatio-temporal correlation. Therefore, construction of a space–time prediction model of groundwater level considering space–time factors and improvement of the prediction accuracy of groundwater level dynamic changes are of considerable theoretical and practical importance for the sustainable development of groundwater resources utilization. Based on the analysis of spatial–temporal characteristics of groundwater level of the pore confined aquifer II of Changwu area in the Yangtze River Delta region of China, the wavelet transform method is used to remove the noise in the original data, and the K-nearest neighbor (KNN) is used to calculate the water level. The spatial–temporal dataset and the long short-term memory (LSTM) are reconstructed by screening the spatial correlation of the monitoring wells in the study area. A spatio-temporal prediction model KNN-LSTM of groundwater level considering spatio-temporal factors is also constructed. The reliability and accuracy of KNN-LSTM, LSTM, support vector regression, and autoregressive integrated moving average model are evaluated by a cross-validation algorithm. Results showed that the prediction accuracy of KNN-LSTM is 20.68%, 46.54%, and 55.34% higher than that of other single prediction models.

Deep learning shows declining groundwater levels in Germany until 2100 due to climate change

In this study we investigate how climate change will directly influence the groundwater resources in Germany during the 21st century. We apply a machine learning groundwater level prediction framework, based on convolutional neural networks to 118 sites well distributed over Germany to assess the groundwater level development under the RCP8.5 scenario, based on six selected climate projections, which represent 80% of the bandwidth of the possible future climate signal for Germany. We consider only direct meteorological inputs, while highly uncertain anthropogenic factors such as groundwater extractions are excluded. We detected significant declining trends of groundwater levels for most of the sites, revealing a spatial pattern of stronger decreases especially in the northern and eastern part of Germany, emphasizing already existing decreasing trends in these regions. We can further show an increased variability and longer periods of low groundwater levels during the annual cycle towards the end of the century.