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A hybrid wavelet–machine learning model for qanat water flow prediction

Samani Saeideh, Vadiati Meysam, Delkash Madjid, Bonakdari Hossein

Acta Geophysica

2023

Vol. 71, no. 4

1895--1913

In many parts of semiarid and arid regions, qanats are the leading supplier of water demand for agricultural and drinking usage. Qanat is an ancient collecting water system, and qanat water flow (QWF) varies in different seasons and decreases gradually by pumping groundwater wells. The present research utilized a set of supervised machine learning (ML) models to predict the QWF in the Chaghlondi Aquifer in Iran using monthly intervals of 14 years (2007–2021). The wavelet transform (WT) technique was also applied to enhance the QWF prediction quality of ML models for three lead months utilizing QWF, precipitation, evapotranspiration, temperature and GWL signal datasets as input. The five widely used ML models, i.e., artificial neural network (ANN), adaptive neuro-fuzzy inference system, group method of data handling (GMDH), gene expression programming and least square support vector machine, were applied and then compared with their hybrid wavelet models. To assess the performance of the models, the following four evaluation criteria were employed: correlation coefficient (R), Nash–Sutcliffe efficiency (NSE), root means squared error (RMSE) and mean absolute error (MAE). The outcomes showed that the hybrid-wavelet ML considerably improved the standalone model performance. The best QWF predictions for a one-month ahead QWF prediction were acquired from the WT-GMDH model results from input scenario 3 with RMSE, MAE, R and NSE equal to 14.46, 10.78, 0.93 and 0.85, respectively. In addition, the result of this study indicates that ML's performance was improved by using wavelet transform for two and three months ahead of QWF predictions.

Integrated preprocessing techniques with linear stochastic approaches in groundwater level forecasting

Azari Arash, Zeynoddin Mohammad, Ebtehaj Isa, Sattar Ahmed M. A., Gharabaghi Bahram, Bonakdari Hossein

Acta Geophysica

2021

Vol. 69, no. 4

1395--1411

Accurate modeling of groundwater level (GWL) is a critical and challenging issue in water resources management. The GWL fuctuations rely on many nonlinear hydrological variables and uncertain factors. Therefore, it is important to use an approach that can reduce the parameters involved in the modeling process and minimize the associated errors. This study presents a novel approach for time series structural analysis, multi-step preprocessing, and GWL modeling. In this study, we identifed the time series deterministic and stochastic terms by employing a one-, two-, and three-step preprocessing techniques (a combination of trend analysis, standardization, spectral analysis, diferencing, and normalization techniques). The application of this approach is tested on the GWL dataset of the Kermanshah plains located in the northwest region of Iran, using monthly observations of 60 piezometric stations from September 1991 to August 2017. By removing the dominant nonstationary factors of the GWL data, a linear model with one autoregressive and one seasonal moving average parameter, detrending, and consecutive non-seasonal and seasonal diferencing were created. The quantitative assessment of this model indicates the high performance in GWL forecasting with the coefcient of determination (R2 ) 0.94, scatter index (SI) 0.0004, mean absolute percentage error (MAPE) 0.0003, root mean squared relative error (RMSRE) 0.0004, and corrected Akaike’s information criterion (AICc) 151. Moreover, the uncertainty and accuracy of the proposed linear-based method are compared with two conventional nonlinear methods, including multilayer perceptron artifcial neural network (MLP-ANN) and adaptive neuro-fuzzy inference systems (ANFIS). The uncertainty of the proposed method in this study was±0.105 compared to±0.114 and±0.126 for the best results of the ANN and the ANFIS models, respectively.