机器学习模型在地下水埋深模拟中的适应性分析

基于京津冀气象、社会资料及地下水埋深数据，构建支持向量机(SVM)、循环神经网络(RNN)和长短期记忆神经网络(LSTM)模型对京津冀地区13个城市地下水埋深进行了模拟，并以确定系数、均方根误差、平均绝对百分比误差、纳什系数对3个模型的适应性进行了评价。结果表明：LSTM模型模拟效果最好，其次是RNN,SVM最差；不同城市基于LSTM模型进行地下水埋深模拟时参数调整最少，适应性最好，SVM模型参数调整最多。将3个模型应用于随机选择的6个测站进行验证，在华北地区浅层地下水埋深模拟方面，LSTM模型模拟精度和可信度最好，适应性最强，是该地区地下水埋深模拟的首选机器学习模型。

Adaptability analysis of machine learning model in groundwater depth simulation

Based on the meteorological data, social data and groundwater depth data of Beijing-Tianjin-Hebei, support vector machine (SVM), recurrent neural network (RNN) and long and short-term memory neural network (LSTM) were constructed to simulate the groundwater depth of 13 cities. The adaptability of three models were evaluated in indicators, such as the determination coefficient, root mean square error, mean absolute percentage error, and Nash coefficient. The results showed that LSTM model performed the best, followed by RNN and SVM. Meanwhile, simulation results of different cities demonstrated the least parameter adjustments and the best adaptability of LSTM model for the groundwater depth simulation, and SVM model parameters had the most parameters adjustments. Three models were applied to 6 randomly selected stations for verification and it was shown that in the shallow groundwater depth simulation in North China, LSTM model had good simulation accuracy and reliability, with strong adaptability. Therefore, it is the first choice in the groundwater depth simulation of the studying area.