IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v35y2021i9d10.1007_s11269-021-02874-8.html
   My bibliography  Save this article

Assessment and Prediction of Groundwater using Geospatial and ANN Modeling

Author

Listed:
  • Ankita P. Dadhich

    (Malaviya National Institute of Technology, J.L.N. Marg)

  • Rohit Goyal

    (Malaviya National Institute of Technology, J.L.N. Marg)

  • Pran N. Dadhich

    (Poornima Institute of Engineering & Technology)

Abstract

In semi-arid regions, the deterioration in groundwater quality and drop in water level upshots the importance of water resource management for drinking and irrigation. Therefore geospatial techniques could be integrated with mathematical models for accurate spatiotemporal mapping of groundwater risk areas at the village level. In the present study, changes in water level, quality patterns, and future trends were analyzed using eight years (2012–2019) groundwater data for 171 villages of the Phagi tehsil, Jaipur district. Kriging interpolation method was used to draw spatial maps for the pre-monsoon season. These datasets were integrated with three different time series forecasting models (Simple Exponential Smoothing, Holt's Trend Method, ARIMA) and Artificial Neural Network models for accurate prediction of groundwater level and quality parameters. Results reveal that the ANN model can describe groundwater level and quality parameters more accurately than the time series forecasting models. The change in groundwater level was observed with more than 4.0 m rise in 81 villages during 2012–2013, whereas ANN predicted results of 2023–2024 predict no rise in water level > 4.0 m. However, based on predicted results of 2024, the water level will drop by more than 6.0 m in 16 villages of Phagi. Assessment of water quality index reveals unfit groundwater in 74% villages for human consumption in 2024. This time series and projected groundwater level and quality at the micro-level can assist decision-makers in sustainable groundwater management.

Suggested Citation

  • Ankita P. Dadhich & Rohit Goyal & Pran N. Dadhich, 2021. "Assessment and Prediction of Groundwater using Geospatial and ANN Modeling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(9), pages 2879-2893, July.
    • Handle: RePEc:spr:waterr:v:35:y:2021:i:9:d:10.1007_s11269-021-02874-8
    DOI: 10.1007/s11269-021-02874-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-021-02874-8
    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/s11269-021-02874-8?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. Ozgur Kisi & Armin Azad & Hamed Kashi & Amir Saeedian & Seyed Ali Asghar Hashemi & Salar Ghorbani, 2019. "Modeling Groundwater Quality Parameters Using Hybrid Neuro-Fuzzy Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(2), pages 847-861, January.
    2. Sudhir Singh & Prashant Srivastava & Avinash Pandey & Sandeep Gautam, 2013. "Integrated Assessment of Groundwater Influenced by a Confluence River System: Concurrence with Remote Sensing and Geochemical Modelling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(12), pages 4291-4313, September.
    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. Shima Kamali & Keyvan Asghari, 2023. "The Effect of Meteorological and Hydrological Drought on Groundwater Storage Under Climate Change Scenarios," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(8), pages 2925-2943, June.
    2. Saeed Mozaffari & Saman Javadi & Hamid Kardan Moghaddam & Timothy O. Randhir, 2022. "Forecasting Groundwater Levels using a Hybrid of Support Vector Regression and Particle Swarm Optimization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(6), pages 1955-1972, April.
    3. Danieli Mara Ferreira & Marcelo Coelho & Cristovao Vicente Scapulatempo Fernandes & Eloy Kaviski & Daniel Henrique Marco Detzel, 2021. "Deterministic and Stochastic Principles to Convert Discrete Water Quality Data into Continuous Time Series," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(11), pages 3633-3647, September.
    4. Lidia Yadira Perez-Aguilar & Wenseslao Plata-Rocha & Sergio Alberto Monjardin-Armenta & Cuauhtémoc Franco-Ochoa, 2022. "Aridity Analysis Using a Prospective Geospatial Simulation Model in This Mid-Century for the Northwest Region of Mexico," Sustainability, MDPI, vol. 14(22), pages 1-22, November.
    5. Yuqi Dong & Jianzhou Wang & Xinsong Niu & Bo Zeng, 2023. "Combined water quality forecasting system based on multiobjective optimization and improved data decomposition integration strategy," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(2), pages 260-287, March.

    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. Auwalu Faisal Koko & Yue Wu & Ghali Abdullahi Abubakar & Akram Ahmed Noman Alabsi & Roknisadeh Hamed & Muhammed Bello, 2021. "Thirty Years of Land Use/Land Cover Changes and Their Impact on Urban Climate: A Study of Kano Metropolis, Nigeria," Land, MDPI, vol. 10(11), pages 1-27, October.
    2. Salah Elsayed & Mohamed Gad & Mohamed Farouk & Ali H. Saleh & Hend Hussein & Adel H. Elmetwalli & Osama Elsherbiny & Farahat S. Moghanm & Moustapha E. Moustapha & Mostafa A. Taher & Ebrahem M. Eid & M, 2021. "Using Optimized Two and Three-Band Spectral Indices and Multivariate Models to Assess Some Water Quality Indicators of Qaroun Lake in Egypt," Sustainability, MDPI, vol. 13(18), pages 1-23, September.
    3. Dilip Kumar Roy & Kowshik Kumar Saha & Mohammad Kamruzzaman & Sujit Kumar Biswas & Mohammad Anower Hossain, 2021. "Hierarchical Fuzzy Systems Integrated with Particle Swarm Optimization for Daily Reference Evapotranspiration Prediction: a Novel Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(15), pages 5383-5407, December.
    4. Xi Zhu & Yansha Wen & Xiang Li & Feng Yan & Shuhe Zhao, 2023. "Remote Sensing Inversion of Typical Water Quality Parameters of a Complex River Network: A Case Study of Qidong’s Rivers," Sustainability, MDPI, vol. 15(8), pages 1-18, April.
    5. Hafiz Umar Farid & Hafiz Usman Ayub & Zahid Mahmood Khan & Ijaz Ahmad & Muhammad Naveed Anjum & Rana Muhammad Asif Kanwar & Muhammad Mubeen & Pervaiz Sakinder, 2023. "Groundwater quality risk assessment using hydro-chemical and geospatial analysis," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(8), pages 8343-8365, August.
    6. Shailendra Pratap & Prashant K. Srivastava & Ashish Routray & Tanvir Islam & Rajesh Kumar Mall, 2020. "Appraisal of hydro-meteorological factors during extreme precipitation event: case study of Kedarnath cloudburst, Uttarakhand, India," 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. 100(2), pages 635-654, January.
    7. Chaitanya B. Pande & Kanak N. Moharir & Sudhir Kumar Singh & Abhay M. Varade, 2020. "An integrated approach to delineate the groundwater potential zones in Devdari watershed area of Akola district, Maharashtra, Central India," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(5), pages 4867-4887, June.
    8. Naser Shiri & Jalal Shiri & Zaher Mundher Yaseen & Sungwon Kim & Il-Moon Chung & Vahid Nourani & Mohammad Zounemat-Kermani, 2021. "Development of artificial intelligence models for well groundwater quality simulation: Different modeling scenarios," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-24, May.
    9. Chaitanya B. Pande & Kanak N. Moharir & Sudhir Kumar Singh & Bloodless Dzwairo, 2020. "Groundwater evaluation for drinking purposes using statistical index: study of Akola and Buldhana districts of Maharashtra, India," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(8), pages 7453-7471, December.
    10. Zubairul Islam & Muthukumarasamy Ranganathan & Murugesan Bagyaraj & Sudhir Kumar Singh & Sandeep Kumar Gautam, 2022. "Multi-decadal groundwater variability analysis using geostatistical method for groundwater sustainability," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(3), pages 3146-3164, March.
    11. Mojtaba Kadkhodazadeh & Saeed Farzin, 2021. "A Novel LSSVM Model Integrated with GBO Algorithm to Assessment of Water Quality Parameters," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(12), pages 3939-3968, September.
    12. Jinjie Miao & Guoliang Liu & Bibo Cao & Yonghong Hao & Jianmimg Chen & Tian−Chyi Yeh, 2014. "Identification of Strong Karst Groundwater Runoff Belt by Cross Wavelet Transform," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(10), pages 2903-2916, August.

    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:35:y:2021:i:9:d:10.1007_s11269-021-02874-8. 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.