The quantitative analyses of karst spring discharge typically rely on physical-based models, which are inherently uncertain. To improve the understanding of the mechanism of spring discharge fluctuation and the relationship between precipitation and spring discharge, three machine learning methods were developed to reduce the predictive errors of physical-based groundwater models, simulate the discharge of Longzici spring’s karst area, and predict changes in the spring on the basis of long time series precipitation monitoring and spring water flow data from 1987 to 2018. The three machine learning methods included two artificial neural networks (ANNs), namely multilayer perceptron (MLP) and long short-term memory–recurrent neural network (LSTM–RNN), and support vector regression (SVR). A normalization method was introduced for data preprocessing to make the three methods robust and computationally efficient. To compare and evaluate the capability of the three machine learning methods, the mean squared error (MSE), mean absolute error (MAE), and root-mean-square error (RMSE) were selected as the performance metrics for these methods. Simulations showed that MLP reduced MSE, MAE, and RMSE to 0.0010, 0.0254, and 0.0318, respectively. Meanwhile, LSTM–RNN reduced MSE to 0.0010, MAE to 0.0272, and RMSE to 0.0329. Moreover, the decrease in MSE, MAE, and RMSE was 0.0397, 0.1694, and 0.1991, respectively, for SVR. Results indicated that MLP performed slightly better than LSTM–RNN, and MLP and LSTM–RNN performed considerably better than SVR. Furthermore, ANNs were demonstrated to be prior machine learning methods for simulating and predicting karst spring discharge.

岩溶泉水排放量的定量分析通常依赖于基于物理的模型，这种模型固有地不确定。为了更好地了解泉水流量波动的机理以及降水与泉水流量之间的关系，开发了三种机器学习方法，以减少基于物理的地下水模型的预测误差，模拟龙齐奇泉岩溶区的流量并预测变化基于1987年至2018年的长时间序列降水监测和春季水流量数据在春季进行。三种机器学习方法包括两个人工神经网络（ANN），即多层感知器（MLP）和长期短期记忆—递归神经网络（LSTM–RNN）和支持向量回归（SVR）。为数据预处理引入了归一化方法，以使这三种方法具有鲁棒性和计算效率。为了比较和评估这三种机器学习方法的能力，选择了均方误差（MSE），平均绝对误差（MAE）和均方根误差（RMSE）作为这些方法的性能指标。仿真显示，MLP将MSE，MAE和RMSE分别降低到0.0010、0.0254和0.0318。同时，LSTM–RNN将MSE降至0.0010，MAE降至0.0272，RMSE降至0.0329。此外，对于SVR，MSE，MAE和RMSE的减少分别为0.0397、0.1694和0.1991。结果表明，MLP的性能略优于LSTM–RNN，MLP和LSTM–RNN的性能明显优于SVR。此外，