IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v277y2023ics0378377422006357.html
   My bibliography  Save this article

Spatiotemporal Forecasting of the Groundwater Quality for Irrigation Purposes, Using Deep Learning Method: Long Short-Term Memory (LSTM)

Author

Listed:
  • Docheshmeh Gorgij, A.
  • Askari, Gh
  • Taghipour, A.A.
  • Jami, M.
  • Mirfardi, M.

Abstract

Present study was conducted to predict the spatiotemporal groundwater suitability for irrigation purpose through deep learning method, Long-Short term memory (LSTM), in northwest of Iran. Sodium Adsorption Ratio (SAR) as a crucial irrigation water quality criterion for 101 sampling point for an 18-year data period from 2002 to 2019, was utilized as the input for deep learning model in order to forecast the irrigation water quality for the next year, 2020. To evaluate the model accuracy in spatiotemporal data forecasting, performance criteria such as Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and R were used which approved the model accuracy by 1.212, 0.312 and 0.89 of MAPE, RMSE and R, respectively. On the other hand, the model capability was assessed by RBIAS and generalization ability (GA), which results showed that LSTM model underestimated the targets with RBIAS equals to about 1.539, while had an acceptable GA, equals to 1.1832. Considering the carried-out map of irrigation water quality for the study area it was revealed that about 78% have the desirable to acceptable quality for irrigation and the about 22% are moderate to non-acceptable. The most non-acceptable points are juxtaposed to the residential area which shows the anthropogenic effect on groundwater quality through the fertilizers and the other pollutants, infiltered into the groundwater resources.

Suggested Citation

  • Docheshmeh Gorgij, A. & Askari, Gh & Taghipour, A.A. & Jami, M. & Mirfardi, M., 2023. "Spatiotemporal Forecasting of the Groundwater Quality for Irrigation Purposes, Using Deep Learning Method: Long Short-Term Memory (LSTM)," Agricultural Water Management, Elsevier, vol. 277(C).
    • Handle: RePEc:eee:agiwat:v:277:y:2023:i:c:s0378377422006357
    DOI: 10.1016/j.agwat.2022.108088
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378377422006357
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.agwat.2022.108088?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. Sayiter Yıldız & Can Bülent Karakuş, 2020. "Estimation of irrigation water quality index with development of an optimum model: a case study," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(5), pages 4771-4786, June.
    2. Chhatra Mani Sharma & Shichang Kang & Lekhendra Tripathee & Rukumesh Paudyal & Mika Sillanpää, 2021. "Major ions and irrigation water quality assessment of the Nepalese Himalayan rivers," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(2), pages 2668-2680, February.
    3. Ping Liu & Jin Wang & Arun Kumar Sangaiah & Yang Xie & Xinchun Yin, 2019. "Analysis and Prediction of Water Quality Using LSTM Deep Neural Networks in IoT Environment," Sustainability, MDPI, vol. 11(7), pages 1-14, April.
    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. Mohammed, Safwan & Arshad, Sana & Bashir, Bashar & Vad, Attila & Alsalman, Abdullah & Harsányi, Endre, 2024. "Machine learning driven forecasts of agricultural water quality from rainfall ionic characteristics in Central Europe," Agricultural Water Management, Elsevier, vol. 293(C).

    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. Kagiso Samuel More & Christian Wolkersdorfer, 2022. "Predicting and Forecasting Mine Water Parameters Using a Hybrid Intelligent System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(8), pages 2813-2826, June.
    2. Allison Lassiter & Nicole Leonard, 2022. "A systematic review of municipal smart water for climate adaptation and mitigation," Environment and Planning B, , vol. 49(5), pages 1406-1430, June.
    3. Francis Rathinam & Sayak Khatua & Zeba Siddiqui & Manya Malik & Pallavi Duggal & Samantha Watson & Xavier Vollenweider, 2021. "Using big data for evaluating development outcomes: A systematic map," Campbell Systematic Reviews, John Wiley & Sons, vol. 17(3), September.
    4. Eric Hitimana & Gaurav Bajpai & Richard Musabe & Louis Sibomana & Jayavel Kayalvizhi, 2021. "Implementation of IoT Framework with Data Analysis Using Deep Learning Methods for Occupancy Prediction in a Building," Future Internet, MDPI, vol. 13(3), pages 1-19, March.
    5. Heelak Choi & Sang-Ik Suh & Su-Hee Kim & Eun Jin Han & Seo Jin Ki, 2021. "Assessing the Performance of Deep Learning Algorithms for Short-Term Surface Water Quality Prediction," Sustainability, MDPI, vol. 13(19), pages 1-11, September.
    6. Xiaonan Ji & Jianghai Chen & Yali Guo, 2022. "A Multi-Dimensional Investigation on Water Quality of Urban Rivers with Emphasis on Implications for the Optimization of Monitoring Strategy," Sustainability, MDPI, vol. 14(7), pages 1-18, March.
    7. Wessam El-Ssawy & Hosam Elhegazy & Heba Abd-Elrahman & Mohamed Eid & Niveen Badra, 2023. "Identification of the best model to predict optical properties of water," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(7), pages 6781-6797, July.
    8. Nadine Bachmann & Shailesh Tripathi & Manuel Brunner & Herbert Jodlbauer, 2022. "The Contribution of Data-Driven Technologies in Achieving the Sustainable Development Goals," Sustainability, MDPI, vol. 14(5), pages 1-33, February.
    9. Song, Chenyu & Zhang, Haiping, 2020. "Study on turbidity prediction method of reservoirs based on long short term memory neural network," Ecological Modelling, Elsevier, vol. 432(C).
    10. El Bilali, Ali & Taleb, Abdeslam & Brouziyne, Youssef, 2021. "Groundwater quality forecasting using machine learning algorithms for irrigation purposes," Agricultural Water Management, Elsevier, vol. 245(C).
    11. Mosleh Hmoud Al-Adhaileh & Fawaz Waselallah Alsaade, 2021. "Modelling and Prediction of Water Quality by Using Artificial Intelligence," Sustainability, MDPI, vol. 13(8), pages 1-18, April.

    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:eee:agiwat:v:277:y:2023:i:c:s0378377422006357. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/agwat .

    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.