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Assessment of prediction performances of stochastic models: Monthly groundwater level prediction in Southern Italy

Boulariah O Mikhailov PA Longobardi A Elizariev AN Aksenov SG

O Boulariah, PA Mikhailov, A Longobardi, et al. 2021: Assessment of prediction performances of stochastic models: Monthly groundwater level prediction in Southern Italy. Journal of Groundwater Science and Engineering, 9(2): 161-170. doi: 10.19637/j.cnki.2305-7068.2021.02.008
Citation: O Boulariah, PA Mikhailov, A Longobardi, et al. 2021: Assessment of prediction performances of stochastic models: Monthly groundwater level prediction in Southern Italy. Journal of Groundwater Science and Engineering, 9(2): 161-170. doi: 10.19637/j.cnki.2305-7068.2021.02.008

doi: 10.19637/j.cnki.2305-7068.2021.02.008

Assessment of prediction performances of stochastic models: Monthly groundwater level prediction in Southern Italy

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  • Figure  1.  Flowchart of applied methodology for forecasting time series

    Figure  2.  ARIMA model prediction method (Chatfield et al.1973)

    Figure  3.  Digital elevation model with location of water wells (right panel) and mean annual precipitation (left panel) in the study area

    Figure  4.  Predicted and observed values of the ground water level.

    Figure  5.  Autocorrelation function graph for the residues of the SARIMA model (1,1,2) (0,1,1)12

    Figure  6.  Forecasting using SARIMA(0,1,3)(0,1,2)12; (a) Acera Capomazzo, (b) Casamicciola, (c) Cassano di Sessa Aurunca, (d) Forio(Calitto), (e) Forio(Pontone), (f) Forio(Umberto I), (g) Nocelleto di Carinola, (h) Parete(tre ponti) scrivi cosa sono le fasce colorate.

    Table  1.   Forecasting models Results based on AIC and BIC criteria

    Name of stationParameters of modelAICBIC
    Acera Capomazzo(1,1,2)(0,1,2)12514.44542.39
    Casamicciola(1,1,2)(0,1,1)12−1228.75−1209.5
    Cassano di Sessa Aurunca(0,1,3)(0,1,2)12914.41942.54
    Forio(Calitto)(1,1,2)(0,1,1)12607.55630.75
    Forio(Pontone)(0,1,3)(0,1,2)12−571.39−547.92
    Forio(Umberto I)(1,1,1)(0,1,1)12−39.8−24.01
    Nocelleto di Carinola(1,1,1)(0,1,1)12−377.44−309.26
    Parete(tre ponti)(1,1,2)(0,1,1)12−702.58−682.21
    下载: 导出CSV

    Table  2.   The comparison of different SARIMA models based on accuracy measures

    Parete
    (tre ponti)
    Nocelleto di carinolaForio (Umberto I)Forio (Pontone)Forio (Calitto)Cassano di Sessa AuruncaCasamicciolaAccera Copmazzo
    SARIMA (0,1,3)(0,1,2)12
    NSE0.9440.9960.9200.9490.9850.9990.9320.984
    MAE0.0650.1100.1310.0670.2170.2550.0260.214
    RMSE0.1070.1830.2180.1080.3630.4200.0370.331
    Pearson cоr.0.9710.9350.9600.9740.9620.9300.9650.983
    MSE0.0110.0320.0470.0110.1210.1760,0010.109
    d0.9990.9990.9990.9990.9990.9990.9990.999
    BIAS$−0.0260.027−0.006−0.012−0.046−0.0450.0360.003
    MSDE4.7E-044.3E-061.5E-053.1E-090.0060.1012.2E-060.016
    R20.9440.8740.9220.9490.92570.8650.9320.967
    AIC−678.64−321.22−36.87−571.61625.55914.41−1203.63532.08
    BIC−654.2−294.18−13.18−547.92652.85941.39−1180.55559.97
    SARIMA (1,1,2)(0,1,1)12
    NSE0.9430.9960.9200.9470.6890.9990.9300.984
    MAE0.0640.1090.1290.0680.5290.2550.0260.211
    RMSE0.1080.1820.2180.11016257740.4220.0370.329
    Pearson cor.0.9710.9340.9600.9730.5530.9290.9640.983
    MSE0.0120.0320.0470.0120.4190.1780.0010.107
    d0.9990.9990.9990.9990.9990.9990.9990.999
    BIAS$−0.0180.032−0.0088−0.013−2.340−0.046−0.1170.057
    MSDE0.00012.6E-061.3E-053.1E-090.1980.1062.2E-060017
    R20.9430.8730.9210.9480.3060.8630.9310.967
    AIC−702.58−327.03−37.95−562.72607.55920.63−1226.74519.45
    BIC−682.21−304.5−18.2−542.97630.3943.12−1207.5542.69
    SARIMA (1,1,2)(0,1,1)12
    NSE0.9430.9960.9200.9460.9850.9990.9400.984
    MAE0.0630.1100.1290.0690.2190.2570.0240.212
    RMSE0.1080.1830.2180.1110.3650.4240.0340.330
    Pearson cor.0.9710.9340.9600.9730.9620.9280.9700.983
    MSE0,0110.0320.0470.0120.1220.1800.0010.107
    d0.9990.9990.9990.9990.9990.9990.9990.999
    BIAS$−0.0180.033−0.008−0.013−0.042−0.0470.0480.061
    MSDE0,00012.4E-061.3E-053.1E-090.0050.1092.2E-060017
    R20.9430.8730.9210.9470.8620.8620.9410.967
    AIC−672.01−327.44−39.8−561.6629.36924.5−1194.32518.6
    BIC−655.71−309.41−24−545.8647.56942.491−1178.931537.19
    下载: 导出CSV

    Table  3.   Statistical index values for the selected SARIMA model (0,1,3) (0,1,2)12

    NSEBIAS%R2dr
    Accera Copmazzo0.980.0030.960.990.98
    Casamicciola0.930.030.930.990.96
    Cassano di Sessa Aurunca0.99−0.040.860.990.93
    Forio Calitto0.98−0.040.920.990.96
    Forio Pontone0.94−0.0120.940.990.97
    Forio Umberto I0.92−0.0060.920.990.96
    Nocelleto di carinola0.990.020.870.990.93
    Parete tre ponti0.94−0.020.940.990.97
    Model Quality (Very good)0.75< NSE<1.00PBIAS<±100.75 < R2≤ 1.01r > 0.7
    下载: 导出CSV
  • Adamowski J. 2008. Development of a short-term river flood forecasting method for snowmelt driven floods based on wavelet and cross-wavelet analysis. Journal of Hydrology, 353: 247-266. doi:  10.1016/j.jhydrol.2008.02.013.
    Ahn H, Salas J. 1997. Groundwater head sampling based on stochastic analysis. Water Resources Research, 33: 2769-2780. doi:  10.1029/97WR02187.
    Balasmeh A, Babbar O, Karmaker T. 2019. Trend analysis and ARIMA modeling for forecasting precipitation pattern in Wadi Shueib catchment area in Jordan. Arabian Journal of Geosciences, 12: 27-27. doi:  10.1007/s12517-018-4205-z.
    Boulariah O, Meddi M, Longobardi A. 2019. Assessment of prediction performances of stochastic and conceptual hydrological models: Monthly stream flow prediction in northwestern Algeria. Arabian Journal of Geosciences, 12: 792-792. doi:  10.1007/s12517-019-4847-5.
    Chatfield C, Prothero DL. 1973. Box‐Jenkins seasonal forecasting: Problems in a case‐study. Journal of the Royal Statistical Society: Series A (General), 136(3): 295-315. doi:  10.2307/2344994.
    Cleophas T, Zwinderman A. 2016. One-Sample Continuous Data (One-Sample T-Test, One-Sample Wilcoxon Signed Rank Test, 10 Patients). SPSS for Starters and 2nd Levelers: 3-6.
    Faruk DÖ. 2010. A hybrid neural network and ARIMA model for water quality time series prediction. Engineering applications of artificial intelligence, 23(4), 586-594.
    Hamilton J. 1994. Time series analysis. Princeton University Press. 1-820.
    Jaiswal R, Lohani A, Tiwari H. 2015. Statistical Analysis for Change Detection and Trend Assessment and Climatological Parameters. Environmental Processes: 729-749.
    Kumar KS, Rathnam EV. 2019. Analysis and prediction of groundwater level trends using four variations of Mann Kendall tests and ARIMA modelling. Journal of the Geological Society of India, 94: 281-289. doi:  10.1007/s12594-019-1308-4.
    Longobardi A, Villani P. 2006. Seasonal response function for daily stream flow investigation. Physics and Chemistry of the Earth, 31: 1107-1117. doi:  10.1016/j.pce.2006.02.063.
    Longobardi A, Van Loon A. 2018. Base flow index vulnerability to variation in dry spell length for a range of catchment and climate properties. Hydrological Processes, 32: 2496-2509. doi:  10.1002/hyp.13147.
    Montgomery DC, Jennings CL, Kulahci M. 2015. Introduction to time series analysis and forecasting. John Wiley & Sons.
    Mirzavand M, Ghazavi R. 2014. A stochastic modelling technique for groundwater level forecasting in an arid environment using time series methods. Water resources management, 29(4): 1315-1328. doi:  10.1007/s11269-014-0875-9.
    Mirzavand M, Ghazavi R. 2015. A stochastic modelling technique for groundwater level forecasting in an arid environment using time series methods. Water resources management, 29(4): 1315-1328.
    Mohammadi K, Eslami H, Dayani DS. 2005. Comparison of regression, ARIMA and ANN models for reservoir inflow forecasting using snowmelt equivalent (a case study of Karaj). Journal of Agricultural Science and Technology, 7: 17-30.
    Mombeni H, Rezaei S, Nadarajah S, et al. 2013. Estimation of water demand in Iran based on SARIMA models. Environmental Modeling & Assessment, 18: 559-565.
    Moriasi D, ARnoid G, Kuijk M, et al. 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 50: 885-900. doi:  10.13031/2013.23153.
    Nash J, Sutcliffe J. 1970. River flow forecasting through conceptual models part I — A discussion of principles. Journal of Hydrology, 10: 282-290. doi:  10.1016/0022-1694(70)90255-6.
    Nunno FD, Granata F. 2020. Groundwater level prediction in Apulia region (Southern Italy) using NARX neural network. Environmental Research, 190: 110062. doi:  10.1016/j.envres.2020.110062.
    Oikonomou PD, Alzraiee AH, Karavitis CA, et al. 2018. A novel framework for filling data gaps in groundwater level observations. Advances in Water Resources, 119: 111-124. doi:  10.1016/j.advwatres.2018.06.008.
    Semiromi M, Koch M. 2019. Reconstruction of groundwater levels to impute missing values using singular and multichannel spectrum analysis: application to the Ardabil Plain, Iran. Hydrological sciences journal, 64: 1711-1726. doi:  10.1080/02626667.2019.1669793.
    Singh J, Knapp H, Arnold J, et al. 2005. Hydrologic modeling of the Iroquois River watershed using HSPF and SWAT. J. American Water Resources Assoc, 41(2): 361-375. doi:  10.1111/j.1752-1688.2005.tb03741.x.
    Suryanarayana C, Sudheer C, Mahammood V, et al. 2014. An integrated wavelet-support vector machine for groundwater level prediction in Visakhapatnam, India. Neurocomputing, 145: 324-335. doi:  10.1016/j.neucom.2014.05.026.
    Takafuji E, Rocha M, Manzione R. 2019. Groundwater level prediction forecasting and assessment of uncertainty using SGS and ARIMA Models: A case study in the Bauru Aquifer System (Brazil). Natural Resources Research, 28: 487-503. doi:  10.1007/s11053-018-9403-6.
    Valipour M, Banihabib M, Behbahani S. 2013. Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir. Journal of Hydrology, 476: 433-441. doi:  10.1016/j.jhydrol.2012.11.017.
    Valipour M. 2013a. Increasing irrigation efficiency by management strategies: cutback and surge irrigation. ARPN Journal of Agricultural and Biological Science. 8(1):35-43.
    Valipour M. 2013b. Necessity of irrigated and rainfed agriculture in the world. Irrigation & Drainage Systems Engineering. S9, e001.
    Valipour M. 2012. Ability of Box-Jenkins models to estimate of reference potential evapotranspiration (A case study: Mehrabad Synoptic Station, Tehran, Iran). IOSR Journal of Agriculture and Veterinary Science, 1.5: 1-11.
    Willmott C. 1981. On the validation of models. Physical Geography, 2: 184-194. doi:  10.1080/02723646.1981.10642213.
    Willmott CJ, Matsuura K. 2005. Advantages of the mean absolute error (MAE) overthe root mean square error (RMSE) in assessingaverage model performance, 30, 79-82.
    Young P. 1999. Nonstationary time series analysis and forecasting. Progress in Environmental Science, 1: 3-48.
    Yang Q, Wang Y, Zhang J, et al. 2017. A comparative study of shallow groundwater level simulation with three time series models in a coastal aquifer of South China. Applied Sciences, 7: 689-698. doi:  10.3390/app7070689.
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出版历程
  • 收稿日期:  2020-11-02
  • 录用日期:  2021-04-20
  • 网络出版日期:  2021-08-04
  • 刊出日期:  2021-06-28

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