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Trend Analysis and Spatial Prediction of Groundwater Levels Using Time Series Forecasting and a Novel Spatio-Temporal Method

Author

Listed:
  • Mohamad Sakizadeh

    (Ton Duc Thang University
    Ton Duc Thang University)

  • Mohamed M. A. Mohamed

    (United Arab Emirates University
    United Arab Emirates University)

  • Harald Klammler

    (Engineering School for Sustainable Infrastructure and Environment (ESSIE), UF
    Federal University of Bahia)

Abstract

Overexploitation of groundwater in the Malayer Plain has resulted in a continuous decline of groundwater levels over recent years with associated risks to water security. Effective water resource management requires the identification of the most susceptible regions and periods to such risks and, hence, spatio-temporal prediction tools of groundwater levels. For this purpose, we use 27 years of groundwater level records (between 1984 and 2012) and apply time series forecasting models including seasonal Autoregressive Integrated Moving Average (ARIMA) and Holt-Winters Exponential Smoothing (HWES). The spatial variation of groundwater levels is investigated by a novel method known as Fixed Rank Kriging (FRK). The results demonstrate that ARIMA outperforms HWES in fitting the training data. In contrast, the 95% confidence bound of ARIMA predictions is wider than that of HWES and ARIMA’s predicted seasonal cycle is weaker. The time series forecasting by a stochastic simulation indicated that if the current situation continues, the level of groundwater is expected to decline from 1635 m to about 1605 m by 2022. The FRK showed that the amount of groundwater extraction in the western part of the aquifer was higher than that of the northern and central parts.

Suggested Citation

  • Mohamad Sakizadeh & Mohamed M. A. Mohamed & Harald Klammler, 2019. "Trend Analysis and Spatial Prediction of Groundwater Levels Using Time Series Forecasting and a Novel Spatio-Temporal Method," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(4), pages 1425-1437, March.
    • Handle: RePEc:spr:waterr:v:33:y:2019:i:4:d:10.1007_s11269-019-02208-9
    DOI: 10.1007/s11269-019-02208-9
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    References listed on IDEAS

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    1. Hossein Tabari & Safar Marofi, 2011. "Changes of Pan Evaporation in the West of Iran," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(1), pages 97-111, January.
    2. Chandan Kumar Singh & Yashwant B. Katpatal, 2017. "A GIS Based Design of Groundwater Level Monitoring Network Using Multi-Criteria Analysis and Geostatistical Method," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(13), pages 4149-4163, October.
    3. Rob J. Hyndman, 2006. "Another Look at Forecast Accuracy Metrics for Intermittent Demand," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 4, pages 43-46, June.
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    Cited by:

    1. Dilip Kumar Roy & Sujit Kumar Biswas & Kowshik Kumar Saha & Khandakar Faisal Ibn Murad, 2021. "Groundwater Level Forecast Via a Discrete Space-State Modelling Approach as a Surrogate to Complex Groundwater Simulation Modelling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(6), pages 1653-1672, April.
    2. Haibo Chu & Jianmin Bian & Qi Lang & Xiaoqing Sun & Zhuoqi Wang, 2022. "Daily Groundwater Level Prediction and Uncertainty Using LSTM Coupled with PMI and Bootstrap Incorporating Teleconnection Patterns Information," Sustainability, MDPI, vol. 14(18), pages 1-16, September.
    3. Kiyoumars Roushangar & Roghayeh Ghasempour & Vahid Nourani, 2022. "Spatiotemporal Analysis of Droughts Over Different Climate Regions Using Hybrid Clustering Method," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(2), pages 473-488, January.
    4. R. Sarma & S. K. Singh, 2022. "A Comparative Study of Data-driven Models for Groundwater Level Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(8), pages 2741-2756, June.
    5. Kiyoumars Roushangar & Roghayeh Ghasempour & Farhad Alizadeh, 2022. "Uncertainty Assessment of the Integrated Hybrid Data Processing Techniques for Short to Long Term Drought Forecasting in Different Climate Regions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(1), pages 273-296, January.

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