Abstract 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.

中文翻译：

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使用时频分析方法和长短期记忆神经网络相结合的岩溶泉流量模拟

摘要 岩溶含水层的泉水流量是降水、地表径流、入渗、地下水流以及人为因素等时空复杂水文过程的结果。这些过程在具有非线性和非平稳特征的多个尺度上随空间和时间变化。为提高岩溶泉水流量预测精度，本研究首先应用奇异谱分析（SSA）和集合经验模态分解（EEMD）等时频分析方法提取娘子关泉流量的频率和趋势特征。然后使用长短期记忆（LSTM）来模拟每个频率和趋势子序列。随后，结合LSTM的模拟结果，完成了对弹簧放电的预测。最后，比较了 LSTM、SSA-LSTM 和 EEMD-LSTM 在不同输入下的性能。结果表明，SSA-LSTM和EEMD-LSTM的性能优于LSTM，EEMD-LSTM模型取得了最好的预测性能。