Integrated preprocessing techniques with linear stochastic approaches in groundwater level forecasting

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

Identyfikatory

DOI

10.1007/s11600-021-00617-2

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

groundwater level; linear modelling; stochastics; preprocessing; unit hydrograph; water resources

PL

poziom wód gruntowych; modelowanie liniowe; stochastyka; przetwarzanie wstępne; hydrogram jednostkowy; zasoby wodne

• Wydawca: Instytut Geofizyki PAN ; Springer
• Czasopismo: Acta Geophysica
• Rocznik: 2021
• Tom: Vol. 69, no. 4
• Strony: 1395--1411
• Opis fizyczny: Bibliogr. 56 poz.

Twórcy

autor

Azari Arash

• Department of Water Engineering, Razi University, Kermanshah, Iran

autor

Zeynoddin Mohammad

• Department of Soils and Agro-Food Engineering, Laval University, Quebec G1V 0A6, Canada

autor

Ebtehaj Isa

• Department of Soils and Agro-Food Engineering, Laval University, Quebec G1V 0A6, Canada

autor

Sattar Ahmed M. A.

• Department of Irrigation and Hydraulics, Faculty of Engineering, Cairo University, Giza 12316, Egypt

autor

Gharabaghi Bahram

• School of Engineering, University of Guelph, Guelph, ON NIG 2W1, Canada

• https://orcid.org/0000-0003-0454-2811

autor

Bonakdari Hossein

• Department of Soils and Agro-Food Engineering, Laval University, Quebec G1V 0A6, Canada

• https://orcid.org/0000-0001-6169-3654

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