Assessment of prediction performances of stochastic models: Monthly groundwater level prediction in Southern Italy Assessment of prediction performances of stochastic models: Monthly groundwater level prediction in Southern Italy
  • ISSN 2305-7068
  • Indexed by ESCI CABI CSA
  • Scopus GeoRef AJ CNKI
Advanced Search
Volume 9 Issue 2
Jun.  2021
Turn off MathJax
Article Contents
Boulariah O, Mikhailov PA, Longobardi A, 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: Boulariah O, Mikhailov PA, Longobardi A, 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

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

doi: 10.19637/j.cnki.2305-7068.2021.02.008
More Information
  • Corresponding author:
  • Received Date: 2020-11-02
  • Accepted Date: 2021-04-20
  • Publish Date: 2021-06-22
  • Stochastic modelling of hydrological time series with insufficient length and data gaps is a serious challenge since these problems significantly affect the reliability of statistical models predicting and forecasting skills. In this paper, we proposed a method for searching the seasonal autoregressive integrated moving average (SARIMA) model parameters to predict the behavior of groundwater time series affected by the issues mentioned. Based on the analysis of statistical indices, 8 stations among 44 available within the Campania region (Italy) have been selected as the highest quality measurements. Different SARIMA models, with different autoregressive, moving average and differentiation orders had been used. By reviewing the criteria used to determine the consistency and goodness-of-fit of the model, it is revealed that the model with specific combination of parameters, SARIMA (0,1,3) (0,1,2) 12, has a high R2 value, larger than 92%, for each of the 8 selected stations. The same model has also good performances for what concern the forecasting skills, with an average NSE of about 96%. Therefore, this study has the potential to provide a new horizon for the simulation and reconstruction of groundwater time series within the investigated area.
  • 加载中
  • 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.
  • [1] Luong Van Viet, 2021: Effects of urbanization on groundwater level in aquifers of Binh Duong Province, Vietnam, Journal of Groundwater Science and Engineering, 9, 20-36.  doi: 10.19637/j.cnki.2305-7068.2021.01.003
    [2] Abdulrahman Th Mohammad, Qassem H Jalut, Nadia L Abbas, 2020: Predicting groundwater level of wells in the Diyala River Basin in eastern Iraq using artificial neural network, Journal of Groundwater Science and Engineering, 8, 87-96.  doi: 10.19637/j.cnki.2305-7068.2020.01.009
    [3] JIANG Ti-sheng, QU Ci-xiao, WANG Ming-yu, SUN Yan-wei, HU Bo, CHU Jun-yao, 2017: Analysis on temporal and spatial variations of groundwater hydrochemical characteristics in the past decade in southern plain of Beijing, China, Journal of Groundwater Science and Engineering, 5, 235-248.
    [4] LI Xiao-yuan, YUE Gao-fan, SU Ran, YU Juan, 2016: Research on Pisha-sandstone’s anti-erodibility based on grey multi-level comprehensive evaluation method, Journal of Groundwater Science and Engineering, 4, 103-109.
    [5] LIU Hong-wei, Klaus Hisby, ZHOU Yang-xiao, MA Zhen, CHEN She-ming, GUO Xu, 2016: Features and evaluation of sea/saltwater intrusion in southern Laizhou Bay, Journal of Groundwater Science and Engineering, 4, 141-148.
    [6] ZHOU Zhi-chao, WANG Ju, SU Rui, GUO Yong-hai, LI Jie-biao, JI Rui-li, ZHANG Ming, DONG Jian-nan, 2016: Study on the residence time of deep groundwater for high-level radioactive waste geological disposal, Journal of Groundwater Science and Engineering, 4, 52-59.
    [7] MA Luan, WANG Guang-cai, SHI Zhe-ming, GUO Yu-ying, XU Qing-yu, HUANG Xu-juan, 2016: Simulation of groundwater level recovery in abandoned mines, Fengfeng coalfield, China, Journal of Groundwater Science and Engineering, 4, 344-353.
    [8] XIA Ri-yuan, 2016: Groundwater resources in karst area in Southern China and sustainable utilization pattern, Journal of Groundwater Science and Engineering, 4, 301-309.
    [9] YI Qing, GE Li-qiang, CHENG Yan-pei, DONG Hua, LIU Kun, ZHANG Jian-kang, YUE Chen, 2015: Compilation of Groundwater Quality Map and study of hydrogeochemical characteristics of groundwater in Asia, Journal of Groundwater Science and Engineering, 3, 176-185.
    [10] CHENG Yan-pei, DONG Hua, 2015: Groundwater system division and compilation of Groundwater Resources Map of Asia, Journal of Groundwater Science and Engineering, 3, 127-135.
    [11] GONG Xiao-ping, JIANG Guang-hui, CHEN Chang-jie, GUO Xiao-jiao, ZHANG Hua-sheng, 2015: Specific yield of phreatic variation zone in karst aquifer with the method of water level analysis, Journal of Groundwater Science and Engineering, 3, 192-201.
    [12] GUO Jiao, SHI Ying-chun, WU Li-jie, 2015: Gravity erosion and lithology in Pisha sandstone in southern Inner Mongolia, Journal of Groundwater Science and Engineering, 3, 45-58.
    [13] YANG Xiang-peng, ZHANG Fa-wang, CHEN Zhen, BI Xue-li, SHI Jian, ZHOU Li-xin, YANG Chen, 2015: Compiling distribution of karst in Southern China and Southeast Asia, Journal of Groundwater Science and Engineering, 3, 280-284.
    [14] Ramasamy Jayakumar, 2015: Groundwater level monitoring-importance global groundwater monitoring network, Journal of Groundwater Science and Engineering, 3, 295-305.
    [15] ZHANG Chuan-mian, GUO Xiao-niu, Richard Henry, James Dendy, 2015: Groundwater modelling to help diagnose contamination problems, Journal of Groundwater Science and Engineering, 3, 285-294.
    [16] HAN Kang-qin, LIU Jian, HAN Lei-lei, HAN Wen-ling, ZHANG Yun-xiao, 2014: Prediction of Impacts Caused by South-to-North Water Diversion on Underground Water Level in Shijiazhuang, Journal of Groundwater Science and Engineering, 2, 27-33.
    [17] CHEN Qu, 2014: Anticipatory Adaptation Approaches to Climate Change--A Review and Discussion of Southern Australia’s Sustainable Water Management and Its Strategies and Shortcomings, Journal of Groundwater Science and Engineering, 2, 54-61.
    [18] Qing YI, Yan-pei CHENG, Jian-kang ZHANG, 2014: Analysis on the Salt Content Characteristics of Southern Saline-Alkali Soil in Datong Basin and Its Causes, Journal of Groundwater Science and Engineering, 2, 63-72.
    [19] Zong-jun Gao, Yong-gui Liu, 2013: Groundwater Flow Driven by Heat, Journal of Groundwater Science and Engineering, 1, 22-27.
    [20] Aizhong Ding, Lirong Cheng, Steve Thornton, Wei Huang, David Lerner, 2013: Groundwater quality Management in China, Journal of Groundwater Science and Engineering, 1, 54-59.
  • 加载中


    通讯作者: 陈斌,
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(6)  / Tables(3)

    Article Metrics

    Article views (206) PDF downloads(28) Cited by()
    Proportional views

    Submission system is out of service now, please submit to our email:, hope your understanding!



    DownLoad:  Full-Size Img  PowerPoint