IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v35y2021i6d10.1007_s11269-021-02787-6.html
   My bibliography  Save this article

Groundwater Level Forecast Via a Discrete Space-State Modelling Approach as a Surrogate to Complex Groundwater Simulation Modelling

Author

Listed:
  • Dilip Kumar Roy

    (Bangladesh Agricultural Research Institute)

  • Sujit Kumar Biswas

    (Bangladesh Agricultural Research Institute)

  • Kowshik Kumar Saha

    (Bangladesh Agricultural Research Institute)

  • Khandakar Faisal Ibn Murad

    (Bangladesh Agricultural Research Institute)

Abstract

Reliable and precise forecasts of future groundwater level fluctuations are crucial constituents of sustainable management of scarce water resources and design of remediation plans. Groundwater simulations and predictions are often performed by employing physically based models, which are not applicable in a majority of water scarce areas around the globe, particularly in the developing countries like Bangladesh due to data limitations. On the other hand, data-driven statistical forecast models have demonstrated their suitability to model nonlinear and complex hydrogeological processes to forecast short- and long-term groundwater level fluctuations. The purpose of this effort is to propose a non-physical based approach by utilizing a discrete Space-State model as a prediction tool to forecast future scenarios of groundwater level fluctuations. The present study utilizes the prediction focused approach of the system identification process in which the overall objective is to develop a pragmatic dynamic system model. The performance of the proposed approach is evaluated for groundwater level data at three observation wells of Tanore upazilla in Rajshahi district, Bangladesh. Historical weekly time series data of groundwater level fluctuations from the three observation wells for 39 (1980–2018) years is used to develop the time series model, which is used for future groundwater level predictions for a period of next 22 years (up to 2040). The findings demonstrate the conceivable applicability of the proposed discrete Space-State modelling approach in forecasting future scenarios of groundwater level fluctuations in the selected observation wells.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:waterr:v:35:y:2021:i:6:d:10.1007_s11269-021-02787-6
    DOI: 10.1007/s11269-021-02787-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-021-02787-6
    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/s11269-021-02787-6?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. Thendiyath Roshni & Madan K. Jha & Ravinesh C. Deo & A. Vandana, 2019. "Development and Evaluation of Hybrid Artificial Neural Network Architectures for Modeling Spatio-Temporal Groundwater Fluctuations in a Complex Aquifer System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(7), pages 2381-2397, May.
    2. Vahid Moosavi & Mehdi Vafakhah & Bagher Shirmohammadi & Negin Behnia, 2013. "A Wavelet-ANFIS Hybrid Model for Groundwater Level Forecasting for Different Prediction Periods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(5), pages 1301-1321, March.
    3. Yicheng Gong & Yongxiang Zhang & Shuangshuang Lan & Huan Wang, 2016. "A Comparative Study of Artificial Neural Networks, Support Vector Machines and Adaptive Neuro Fuzzy Inference System for Forecasting Groundwater Levels near Lake Okeechobee, Florida," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(1), pages 375-391, January.
    4. Samad Emamgholizadeh & Khadije Moslemi & Gholamhosein Karami, 2014. "Prediction the Groundwater Level of Bastam Plain (Iran) by Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(15), pages 5433-5446, December.
    5. 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.
    6. Georgios N. Kouziokas & Alexander Chatzigeorgiou & Konstantinos Perakis, 2018. "Multilayer Feed Forward Models in Groundwater Level Forecasting Using Meteorological Data in Public Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(15), pages 5041-5052, December.
    7. Yicheng Gong & Yongxiang Zhang & Shuangshuang Lan & Huan Wang, 2016. "A Comparative Study of Artificial Neural Networks, Support Vector Machines and Adaptive Neuro Fuzzy Inference System for Forecasting Groundwater Levels near Lake Okeechobee, Florida," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(1), pages 375-391, January.
    8. S. Mohanty & Madan Jha & S. Raul & R. Panda & K. Sudheer, 2015. "Using Artificial Neural Network Approach for Simultaneous Forecasting of Weekly Groundwater Levels at Multiple Sites," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(15), pages 5521-5532, December.
    9. Bagher Shirmohammadi & Mehdi Vafakhah & Vahid Moosavi & Alireza Moghaddamnia, 2013. "Application of Several Data-Driven Techniques for Predicting Groundwater Level," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(2), pages 419-432, January.
    10. Sandra M. Guzman & Joel O. Paz & Mary Love M. Tagert, 2017. "The Use of NARX Neural Networks to Forecast Daily Groundwater Levels," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(5), pages 1591-1603, March.
    11. Sheelabhadra Mohanty & Madan Jha & Ashwani Kumar & K. Sudheer, 2010. "Artificial Neural Network Modeling for Groundwater Level Forecasting in a River Island of Eastern India," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(9), pages 1845-1865, July.
    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.

    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. Saeideh Samani & Meysam Vadiati & Farahnaz Azizi & Efat Zamani & Ozgur Kisi, 2022. "Groundwater Level Simulation Using Soft Computing Methods with Emphasis on Major Meteorological Components," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(10), pages 3627-3647, August.
    2. Sandra M. Guzman & Joel O. Paz & Mary Love M. Tagert, 2017. "The Use of NARX Neural Networks to Forecast Daily Groundwater Levels," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(5), pages 1591-1603, March.
    3. Kostić, Srđan & Stojković, Milan & Guranov, Iva & Vasović, Nebojša, 2019. "Revealing the background of groundwater level dynamics: Contributing factors, complex modeling and engineering applications," Chaos, Solitons & Fractals, Elsevier, vol. 127(C), pages 408-421.
    4. Kusum Pandey & Shiv Kumar & Anurag Malik & Alban Kuriqi, 2020. "Artificial Neural Network Optimized with a Genetic Algorithm for Seasonal Groundwater Table Depth Prediction in Uttar Pradesh, India," Sustainability, MDPI, vol. 12(21), pages 1-24, October.
    5. Ao, Chang & Zeng, Wenzhi & Wu, Lifeng & Qian, Long & Srivastava, Amit Kumar & Gaiser, Thomas, 2021. "Time-delayed machine learning models for estimating groundwater depth in the Hetao Irrigation District, China," Agricultural Water Management, Elsevier, vol. 255(C).
    6. Wen-Ping Tsai & Yen-Ming Chiang & Jun-Lin Huang & Fi-John Chang, 2016. "Exploring the Mechanism of Surface and Ground Water through Data-Driven Techniques with Sensitivity Analysis for Water Resources Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(13), pages 4789-4806, October.
    7. Hsi-Ting Fang & Bing-Chen Jhong & Yih-Chi Tan & Kai-Yuan Ke & Mo-Hsiung Chuang, 2019. "A Two-Stage Approach Integrating SOM- and MOGA-SVM-Based Algorithms to Forecast Spatial-temporal Groundwater Level with Meteorological Factors," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(2), pages 797-818, January.
    8. Akram Seifi & Mohammad Ehteram & Vijay P. Singh & Amir Mosavi, 2020. "Modeling and Uncertainty Analysis of Groundwater Level Using Six Evolutionary Optimization Algorithms Hybridized with ANFIS, SVM, and ANN," Sustainability, MDPI, vol. 12(10), pages 1-42, May.
    9. Haijiao Yu & Xiaohu Wen & Qi Feng & Ravinesh C. Deo & Jianhua Si & Min Wu, 2018. "Comparative Study of Hybrid-Wavelet Artificial Intelligence Models for Monthly Groundwater Depth Forecasting in Extreme Arid Regions, Northwest China," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(1), pages 301-323, January.
    10. Xianming Dou & Yongguo Yang & Jinhui Luo, 2018. "Estimating Forest Carbon Fluxes Using Machine Learning Techniques Based on Eddy Covariance Measurements," Sustainability, MDPI, vol. 10(1), pages 1-26, January.
    11. Mohammad Naderianfar & Jamshid Piri & Ozgur Kisi, 2017. "Pre-processing data to predict groundwater levels using the fuzzy standardized evapotranspiration and precipitation index (SEPI)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(14), pages 4433-4448, November.
    12. Gokmen Tayfur & Ata Nadiri & Asghar Moghaddam, 2014. "Supervised Intelligent Committee Machine Method for Hydraulic Conductivity Estimation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(4), pages 1173-1184, March.
    13. Dilip Kumar Roy & Bithin Datta, 2017. "Fuzzy C-Mean Clustering Based Inference System for Saltwater Intrusion Processes Prediction in Coastal Aquifers," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(1), pages 355-376, January.
    14. Afshin Khoshand, 2021. "Application of artificial intelligence in groundwater ecosystem protection: a case study of Semnan/Sorkheh plain, Iran," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(11), pages 16617-16631, November.
    15. Sangita Dey & Arabin Kumar Dey & Rajesh Kumar Mall, 2021. "Modeling Long-term Groundwater Levels By Exploring Deep Bidirectional Long Short-Term Memory using Hydro-climatic Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(10), pages 3395-3410, August.
    16. Samad Emamgholizadeh & Khadije Moslemi & Gholamhosein Karami, 2014. "Prediction the Groundwater Level of Bastam Plain (Iran) by Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(15), pages 5433-5446, December.
    17. Shahab Araghinejad & Nima Fayaz & Seyed-Mohammad Hosseini-Moghari, 2018. "Development of a Hybrid Data Driven Model for Hydrological Estimation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(11), pages 3737-3750, September.
    18. Zhang, Zixiong & Gong, Yicheng & Wang, Zhongjing, 2018. "Accessible remote sensing data based reference evapotranspiration estimation modelling," Agricultural Water Management, Elsevier, vol. 210(C), pages 59-69.
    19. Chih-Chiang Wei & Nien-Sheng Hsu & Chien-Lin Huang, 2014. "Two-Stage Pumping Control Model for Flood Mitigation in Inundated Urban Drainage Basins," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(2), pages 425-444, January.
    20. S. Mohanty & Madan Jha & S. Raul & R. Panda & K. Sudheer, 2015. "Using Artificial Neural Network Approach for Simultaneous Forecasting of Weekly Groundwater Levels at Multiple Sites," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(15), pages 5521-5532, December.

    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:6:d:10.1007_s11269-021-02787-6. 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.