Groundwater is the world's central supply of fresh water. Water supply policies, particularly in dry seasons, thus need to be based on accurate modelling of groundwater level (GWL) fluctuations. In the work reported in this paper, a hybrid wavelet-transform-based extreme learning machine (ELM) model was investigated for predicting GWL. Two other popular models – a wavelet-transform based artificial neural network and a wavelet-transform-based adaptive neuro-fuzzy interference system – were used to evaluate the model. GWL data and mean temperatures of observation wells in an Iranian watershed between 1981 and 2017 were used in the study. The performance of the models was assessed be evaluating their root mean square error, correlation coefficient and mean absolute error. The wavelet-transform-based ELM model outperformed the other two models with a correlation coefficient of 0.983 during a 1 month period. The model was also superior to the others in terms of training and testing speeds.

中文翻译：

---

基于混合神经网络模型的地下水位预测比较研究

地下水是世界淡水的主要供应源。供水政策，特别是在旱季，因此需要基于地下水位 (GWL) 波动的准确模型。在本文报道的工作中，研究了一种基于混合小波变换的极限学习机 (ELM) 模型来预测 GWL。另外两个流行的模型——基于小波变换的人工神经网络和基于小波变换的自适应神经模糊干扰系统——用于评估模型。该研究使用了 1981 年至 2017 年间伊朗流域观测井的 GWL 数据和平均温度。通过评估它们的均方根误差、相关系数和平均绝对误差来评估模型的性能。基于小波变换的 ELM 模型在 1 个月期间的相关系数为 0.983，优于其他两个模型。该模型在训练和测试速度方面也优于其他模型。