Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran.
School of Geology, College of Science, University of Tehran, Tehran, Iran.
Sci Total Environ. 2017 Dec 1;599-600:20-31. doi: 10.1016/j.scitotenv.2017.04.189. Epub 2017 Apr 29.
Accurate prediction of groundwater level (GWL) fluctuations can play an important role in water resources management. The aims of the research are to evaluate the performance of different hybrid wavelet-group method of data handling (WA-GMDH) and wavelet-extreme learning machine (WA-ELM) models and to combine different wavelet based models for forecasting the GWL for one, two and three months step-ahead in the Maragheh-Bonab plain, NW Iran, as a case study. The research used totally 367 monthly GWLs (m) datasets (Sep 1985-Mar 2016) which were split into two subsets; the first 312 datasets (85% of total) were used for model development (training) and the remaining 55 ones (15% of total) for model evaluation (testing). The stepwise selection was used to select appropriate lag times as the inputs of the proposed models. The performance criteria such as coefficient of determination (R), root mean square error (RMSE) and Nash-Sutcliffe efficiency coefficient (NSC) were used for assessing the efficiency of the models. The results indicated that the ELM models outperformed GMDH models. To construct the hybrid wavelet based models, the inputs and outputs were decomposed into sub-time series employing different maximal overlap discrete wavelet transform (MODWT) functions, namely Daubechies, Symlet, Haar and Dmeyer of different orders at level two. Subsequently, these sub-time series were served in the GMDH and ELM models as an input dataset to forecast the multi-step-ahead GWL. The wavelet based models improved the performances of GMDH and ELM models for multi-step-ahead GWL forecasting. To combine the advantages of different wavelets, a least squares boosting (LSBoost) algorithm was applied. The use of the boosting multi-WA-neural network models provided the best performances for GWL forecasts in comparison with single WA-neural network-based models.
准确预测地下水位（GWL）波动对于水资源管理具有重要意义。本研究旨在评估不同混合小波群数据处理（WA-GMDH）和小波极限学习机（WA-ELM）模型的性能，并将不同基于小波的模型结合起来，以预测伊朗西北部马拉盖赫-博纳布平原的 GWL，作为一个案例研究。研究使用了总共 367 个每月 GWL（m）数据集（1985 年 9 月至 2016 年 3 月），将其分为两个子集；前 312 个数据集（总数据集的 85%）用于模型开发（训练），其余 55 个数据集（总数据集的 15%）用于模型评估（测试）。逐步选择用于选择适当的滞后时间作为提出模型的输入。采用决定系数（R）、均方根误差（RMSE）和纳什-苏特克里夫效率系数（NSC）等性能标准来评估模型的效率。结果表明，ELM 模型优于 GMDH 模型。为构建混合小波模型，采用不同最大重叠离散小波变换（MODWT）函数（Daubechies、Symlet、Haar 和 Dmeyer，不同阶数）将输入和输出分解为子时间序列。随后，这些子时间序列被用作 GMDH 和 ELM 模型的输入数据集，以预测多步 GWL。基于小波的模型提高了 GMDH 和 ELM 模型对多步 GWL 预测的性能。为了结合不同小波的优势，应用了最小二乘提升（LSBoost）算法。与单 WA-神经网络模型相比，使用提升多 WA-神经网络模型为 GWL 预测提供了最佳性能。
