北方岩溶泉流量预测的机器学习方法比较
Comparison of Machine Learning Methods for Predicting Karst Spring Discharge in North China
摘要：
岩溶泉流量的定量分析通常依赖于物理模型，这是固有的不确定性。为了提高对泉流量波动机制以及降水与泉流量之间关系的理解，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，模拟龙子祠泉岩溶区的流量，并根据1987 - 2018年的长时间序列降水监测和泉水流量资料预测春季的变化。这三种机器学习方法包括两种人工神经网络，即多层感知器（MLP）和长短期记忆递归神经网络（LSTM-RNN），以及支持向量回归（SVR）。为了提高三种方法的鲁棒性和计算效率，引入了归一化方法进行数据预处理。为了比较和评估三种机器学习方法的能力，选择均方误差（MSE）、平均绝对误差（MAE）和均方根误差（RMSE）作为这些方法的性能度量。仿真结果表明，MLP将MSE、MAE和RMSE分别降低到0.0010、0.0254和0.0318。同时，LSTM-RNN将MSE降低到0.0010，MAE降低到0.0272，RMSE降低到0.0329。此外，对于SVR，MSE、MAE和RMSE的降低分别为0.0910、0.1852和0.3017。结果表明，MLP的性能略好于LSTM-RNN，MLP和LSTM-RNN的性能明显优于SVR。此外，人工神经网络被证明是优先机器学习方法模拟和预测岩溶泉流量。
英文摘要：
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 were 0.0910, 0.1852, and 0.3017, 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
