Simulation of karst spring discharge using a combination of time–frequency analysis methods and long short-term memory neural networks

Spring discharges from karst aquifers are results of spatially and temporally complex hydrologic processes, such as precipitation, surface runoff, infiltration, groundwater flow as well as anthropogenic factors. These processes are spatially and temporally varying at a multiplicity of scales with nonlinear and nonstationary characteristics. For improving the prediction accuracy of karst springs discharge, this study first applied the time–frequency analysis methods, including singular spectrum analysis (SSA) and ensemble empirical mode decomposition (EEMD) to extract frequency and trend feature of Niangziguan Springs discharge. Then the long short-term memory (LSTM) was used to simulate each frequency and trend subsequence. Subsequently, the prediction of spring discharge was completed by a combination of the simulated results from LSTM. Finally, the performances of LSTM, SSA-LSTM, and EEMD-LSTM under different inputs were compared. The results show that the performance of SSA-LSTM and EEMD-LSTM are better than LSTM, and the EEMD-LSTM model achieved the best prediction performance.