IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v12y2022i1p36-d1011841.html
   My bibliography  Save this article

Monitoring of Inland Excess Water Inundations Using Machine Learning Algorithms

Author

Listed:
  • Balázs Kajári

    (Department of Geoinformatics, Physical and Environmental Geography, University of Szeged, Egyetem u. 2-6, 6722 Szeged, Hungary
    Center for Irrigation and Water Management, Institute of Environmental Sciences, Research, Hungarian University of Agriculture and Life Sciences, Annaliget 35, 5540 Szarvas, Hungary)

  • Csaba Bozán

    (Center for Irrigation and Water Management, Institute of Environmental Sciences, Research, Hungarian University of Agriculture and Life Sciences, Annaliget 35, 5540 Szarvas, Hungary)

  • Boudewijn Van Leeuwen

    (Department of Geoinformatics, Physical and Environmental Geography, University of Szeged, Egyetem u. 2-6, 6722 Szeged, Hungary)

Abstract

Nowadays, climate change not only leads to riverine floods and flash floods but also to inland excess water (IEW) inundations and drought due to extreme hydrological processes. The Carpathian Basin is extremely affected by fast-changing weather conditions during the year. IEW (sometimes referred to as water logging) is formed when, due to limited runoff, infiltration, and evaporation, surplus water remains on the surface or in places where groundwater flowing to lower areas appears on the surface by leaking through porous soil. In this study, eight different machine learning approaches were applied to derive IEW inundations on three different dates in 2021 (23 February, 7 March, 20 March). Index-based approaches are simple and provide relatively good results, but they need to be adapted to specific circumstances for each area and date. With an overall accuracy of 0.98, a Kappa of 0.65, and a QADI score of 0.020, the deep learning method Convolutional Neural Network (CNN) gave the best results, compared to the more traditional machine learning approaches Maximum Likelihood (ML), Random Forest (RF), Support Vector Machine (SVM) and artificial neural network (ANN) that were evaluated. The CNN-based IEW maps can be used in operational inland excess water control by water management authorities.

Suggested Citation

  • Balázs Kajári & Csaba Bozán & Boudewijn Van Leeuwen, 2022. "Monitoring of Inland Excess Water Inundations Using Machine Learning Algorithms," Land, MDPI, vol. 12(1), pages 1-22, December.
    • Handle: RePEc:gam:jlands:v:12:y:2022:i:1:p:36-:d:1011841
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/12/1/36/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-445X/12/1/36/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Houk, Eric & Frasier, Marshall & Schuck, Eric, 2006. "The agricultural impacts of irrigation induced waterlogging and soil salinity in the Arkansas Basin," Agricultural Water Management, Elsevier, vol. 85(1-2), pages 175-183, September.
    Full references (including those not matched with items on IDEAS)

    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. Peragón, Juan M. & Pérez-Latorre, Francisco J. & Delgado, Antonio & Tóth, Tibor, 2018. "Best management irrigation practices assessed by a GIS-based decision tool for reducing salinization risks in olive orchards," Agricultural Water Management, Elsevier, vol. 202(C), pages 33-41.
    2. Singh, Ajay, 2016. "Managing the water resources problems of irrigated agriculture through geospatial techniques: An overview," Agricultural Water Management, Elsevier, vol. 174(C), pages 2-10.
    3. Mohammed Al-Murad & Saif Uddin & Tariq Rashid & Habib Al-Qallaf & Abdullah Bushehri, 2017. "Waterlogging in Arid Agriculture Areas Due to Improper Groundwater Management—An Example from Kuwait," Sustainability, MDPI, vol. 9(11), pages 1-12, November.
    4. M. Qadir & E. Quillérou & V. Nangia & G. Murtaza & M. Singh & R.J. Thomas & P. Drechsel & A.D. Noble, 2014. "Economics of salt‐induced land degradation and restoration," Natural Resources Forum, Blackwell Publishing, vol. 38(4), pages 282-295, November.
    5. S. A. Prathapar & N. Rajmohan & B. R. Sharma & P. K. Aggarwal, 2018. "Vertical drains to minimize duration of seasonal waterlogging in Eastern Ganges Basin flood plains: a field experiment," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 92(1), pages 1-17, May.
    6. Jumeniyaz Seydehmet & Guang Hui Lv & Ilyas Nurmemet & Tayierjiang Aishan & Abdulla Abliz & Mamat Sawut & Abdugheni Abliz & Mamattursun Eziz, 2018. "Model Prediction of Secondary Soil Salinization in the Keriya Oasis, Northwest China," Sustainability, MDPI, vol. 10(3), pages 1-22, February.
    7. M. Qadir & E. Quillérou & V. Nangia & G. Murtaza & M. Singh & R.J. Thomas & P. Drechsel & A.D. Noble, 2014. "Economics of salt‐induced land degradation and restoration," Natural Resources Forum, Blackwell Publishing, vol. 0(4), pages 282-295, November.
    8. Wichelns, Dennis & Qadir, Manzoor, 2015. "Achieving sustainable irrigation requires effective management of salts, soil salinity, and shallow groundwater," Agricultural Water Management, Elsevier, vol. 157(C), pages 31-38.
    9. Aein, Reza & Alizadeh, Hosein, 2021. "Integrated hydro-economic modeling for optimal design of development scheme of salinity affected irrigated agriculture in Helleh River Basin," Agricultural Water Management, Elsevier, vol. 243(C).
    10. Singh, Ajay, 2018. "Assessment of different strategies for managing the water resources problems of irrigated agriculture," Agricultural Water Management, Elsevier, vol. 208(C), pages 187-192.

    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:gam:jlands:v:12:y:2022:i:1:p:36-:d:1011841. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.