IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v33y2019i4d10.1007_s11269-019-02208-9.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-019-02208-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11269-019-02208-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Corani, Giorgio & Azzimonti, Dario & Rubattu, Nicolò, 2024. "Probabilistic reconciliation of count time series," International Journal of Forecasting, Elsevier, vol. 40(2), pages 457-469.
    2. Kim, Sungil & Kim, Heeyoung, 2016. "A new metric of absolute percentage error for intermittent demand forecasts," International Journal of Forecasting, Elsevier, vol. 32(3), pages 669-679.
    3. Mojtaba Shadmani & Safar Marofi & Majid Roknian, 2012. "Trend Analysis in Reference Evapotranspiration Using Mann-Kendall and Spearman’s Rho Tests in Arid Regions of Iran," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(1), pages 211-224, January.
    4. Gaetano Perone, 2022. "Comparison of ARIMA, ETS, NNAR, TBATS and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 23(6), pages 917-940, August.
    5. López Menéndez, Ana Jesús & Pérez Suárez, Rigoberto, 2017. "Forecasting Performance and Information Measures. Revisiting the M-Competition /Evaluación de Predicciones y Medidas de Información. Reexamen de la M-Competición," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 35, pages 299-314, Mayo.
    6. Jože Martin Rožanec & Blaž Fortuna & Dunja Mladenić, 2022. "Reframing Demand Forecasting: A Two-Fold Approach for Lumpy and Intermittent Demand," Sustainability, MDPI, vol. 14(15), pages 1-21, July.
    7. Bardia Bayat & Mohsen Nasseri & Khosrow Hosseini & Hojat Karami, 2021. "Nested Augmentation of Rainfall Monitoring Network: Proposing a Hybrid Implementation of Block Kriging and Entropy Theory," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(13), pages 4665-4680, October.
    8. Schipfer, Fabian & Kranzl, Lukas & Olsson, Olle & Lamers, Patrick, 2020. "The European wood pellets for heating market - Price developments, trade and market efficiency," Energy, Elsevier, vol. 212(C).
    9. Emilian Dobrescu, 2014. "Attempting to Quantify the Accuracy of Complex Macroeconomic Forecasts," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 5-21, December.
    10. Jianzhu Li & Senming Tan & Zhaozhen Wei & Fulong Chen & Ping Feng, 2014. "A New Method of Change Point Detection Using Variable Fuzzy Sets Under Environmental Change," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(14), pages 5125-5138, November.
    11. Dooti Roy & Gregory Vaughan & Jianan Hui & Junxian Geng, 2023. "An exploration of National Weather Service daily forecasts using R Shiny," Computational Statistics, Springer, vol. 38(3), pages 1173-1191, September.
    12. Élise Fortin & Robert W Platt & Patricia S Fontela & David L Buckeridge & Caroline Quach, 2015. "Predicting Antimicrobial Resistance Prevalence and Incidence from Indicators of Antimicrobial Use: What Is the Most Accurate Indicator for Surveillance in Intensive Care Units?," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-12, December.
    13. Jiao, Xiaoying & Chen, Jason Li & Li, Gang, 2021. "Forecasting tourism demand: Developing a general nesting spatiotemporal model," Annals of Tourism Research, Elsevier, vol. 90(C).
    14. Victor Richmond R. Jose, 2017. "Percentage and Relative Error Measures in Forecast Evaluation," Operations Research, INFORMS, vol. 65(1), pages 200-211, February.
    15. Altay, Nezih & Litteral, Lewis A. & Rudisill, Frank, 2012. "Effects of correlation on intermittent demand forecasting and stock control," International Journal of Production Economics, Elsevier, vol. 135(1), pages 275-283.
    16. Patrick Urrutia & David Wren & Chrysafis Vogiatzis & Ruriko Yoshida, 2022. "SARS-CoV-2 Dissemination Using a Network of the US Counties," SN Operations Research Forum, Springer, vol. 3(2), pages 1-23, June.
    17. Namhyun Ahn & So Yeon Jo & Suk-Ju Kang, 2019. "Constraint-Aware Electricity Consumption Estimation for Prevention of Overload by Electric Vehicle Charging Station," Energies, MDPI, vol. 12(6), pages 1-18, March.
    18. Michel Fliess & Cédric Join & Cyril Voyant, 2018. "Prediction bands for solar energy: New short-term time series forecasting techniques," Post-Print hal-01736518, HAL.
    19. Ducharme, Corey & Agard, Bruno & Trépanier, Martin, 2021. "Forecasting a customer's Next Time Under Safety Stock," International Journal of Production Economics, Elsevier, vol. 234(C).
    20. Kaur, Lakhvinder & Rishi, Madhuri S. & Chaudhary, Bhagwan Singh, 2022. "Assessment of meteorological and agricultural droughts using remote sensing and their impact on groundwater in an agriculturally productive part of Northwest India," Agricultural Water Management, Elsevier, vol. 274(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:waterr:v:33:y:2019:i:4:d:10.1007_s11269-019-02208-9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.