IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v36y2022i11d10.1007_s11269-022-03204-2.html
   My bibliography  Save this article

Groundwater Level Fluctuations in Coastal Aquifer: Using Artificial Neural Networks to Predict the Impacts of Climatical CMIP6 Scenarios

Author

Listed:
  • Adib Roshani

    (Babol Noshirvani University of Technology)

  • Mehdi Hamidi

    (Babol Noshirvani University of Technology)

Abstract

Groundwater resources play a crucial role in supplying water for domestic, industrial, and agricultural use. In this study ACCESS-CM2, HadGEM3-GC31-LL, and NESM3 were selected for validation from Coupled Model Intercomparison Project Phase 6 (CMIP6). In the following, the feedforward neural network was employed to predict monthly groundwater level (GWL) based on the emission scenarios of the sixth IPCC report (SSP2-4.5 and SSp5-8.5) for the next two decades (2021–2040) in the Sari-Neka coastal aquifer near the Caspian Sea, Iran. In this regard, the monthly maximum and minimum temperature, precipitation, and water table of previous month from four piezometers from 2000 to 2019 were used as input variables to forecast GWL. The evaluation of the three GCM models demonstrated that the ACCESS-CM2 provided the best values of the R2 and RMSE with observation parameters. The results of r, R2, RMSE, and MAE were evaluated for the model and indicated good performance of the model. The results also illustrated that under such mentioned scenarios, the mean monthly temperature would rise approximately from 0.1–1.2 °C. In addition, the mean monthly precipitation is likely to witness changes from -10% to 78% in the next two decades. As a result, this seems to lead to improvement and recharge of groundwater level for the near future. The results can help managers and policymakers to identify adaptation strategies more precisely for basins with similar climates.

Suggested Citation

  • Adib Roshani & Mehdi Hamidi, 2022. "Groundwater Level Fluctuations in Coastal Aquifer: Using Artificial Neural Networks to Predict the Impacts of Climatical CMIP6 Scenarios," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(11), pages 3981-4001, September.
  • Handle: RePEc:spr:waterr:v:36:y:2022:i:11:d:10.1007_s11269-022-03204-2
    DOI: 10.1007/s11269-022-03204-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-022-03204-2
    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/s11269-022-03204-2?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. Babatunde J. Abiodun & Jimmy Adegoke & Abayomi A. Abatan & Chidi A. Ibe & Temitope S. Egbebiyi & Francois Engelbrecht & Izidine Pinto, 2017. "Potential impacts of climate change on extreme precipitation over four African coastal cities," Climatic Change, Springer, vol. 143(3), pages 399-413, August.
    2. Li, Su-Yuan & Miao, Li-Juan & Jiang, Zhi-Hong & Wang, Guo-Jie & Gnyawali, Kaushal Raj & Zhang, Jing & Zhang, Hui & Fang, Ke & He, Yu & Li, Chun, 2020. "Projected drought conditions in Northwest China with CMIP6 models under combined SSPs and RCPs for 2015–2099," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 11(3), pages 210-217.
    3. Hassan Alimohammadi & Ali Reza Massah Bavani & Abbas Roozbahani, 2020. "Mitigating the Impacts of Climate Change on the Performance of Multi-Purpose Reservoirs by Changing the Operation Policy from SOP to MLDR," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(4), pages 1495-1516, March.
    4. Ferenc Szidarovszky & Emery A. Coppola Jr & Jingjie Long & Anthony D Hall & Mary M. Poulton, 2007. "A Hybrid Artificial Neural Network-Numerical Model for Ground Water Problems," Published Paper Series 2007-6, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
    5. Gholamreza Roshan & AbdolAzim Ghanghermeh & Touraj Nasrabadi & Jafar Meimandi, 2013. "Effect of Global Warming on Intensity and Frequency Curves of Precipitation, Case Study of Northwestern Iran," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(5), pages 1563-1579, March.
    6. Goutam Konapala & Ashok K. Mishra & Yoshihide Wada & Michael E. Mann, 2020. "Climate change will affect global water availability through compounding changes in seasonal precipitation and evaporation," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
    7. 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.
    8. Michael Brusco & Douglas Steinley, 2007. "A Comparison of Heuristic Procedures for Minimum Within-Cluster Sums of Squares Partitioning," Psychometrika, Springer;The Psychometric Society, vol. 72(4), pages 583-600, December.
    9. Kondwani Msowoya & Kaveh Madani & Rahman Davtalab & Ali Mirchi & Jay R. Lund, 2016. "Climate Change Impacts on Maize Production in the Warm Heart of Africa," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(14), pages 5299-5312, November.
    10. 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.
    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. Aikaterini Lyra & Athanasios Loukas, 2023. "Simulation and Evaluation of Water Resources Management Scenarios Under Climate Change for Adaptive Management of Coastal Agricultural Watersheds," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(6), pages 2625-2642, May.

    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. 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.
    2. 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.
    3. 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.
    4. 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.
    5. Vahid Habibi & Hasan Ahmadi & Mohammad Jafari & Abolfazl Moeini, 2019. "Application of nonlinear models and groundwater index to predict desertification case study: Sharifabad watershed," 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. 99(2), pages 715-733, November.
    6. Seyed Ahmad Soleymani & Shidrokh Goudarzi & Mohammad Hossein Anisi & Wan Haslina Hassan & Mohd Yamani Idna Idris & Shahaboddin Shamshirband & Noorzaily Mohamed Noor & Ismail Ahmedy, 2016. "A Novel Method to Water Level Prediction using RBF and FFA," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(9), pages 3265-3283, July.
    7. 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.
    8. Ping Lan & Li Guo & Yaling Zhang & Guanghua Qin & Xiaodong Li & Carlos R. Mello & Elizabeth W. Boyer & Yehui Zhang & Bihang Fan, 2024. "Updating probable maximum precipitation for Hong Kong under intensifying extreme precipitation events," Climatic Change, Springer, vol. 177(2), pages 1-20, February.
    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. Pengcheng Qin & Hongmei Xu & Min Liu & Lüliu Liu & Chan Xiao & Iman Mallakpour & Matin Rahnamay Naeini & Kuolin Hsu & Soroosh Sorooshian, 2022. "Projected impacts of climate change on major dams in the Upper Yangtze River Basin," Climatic Change, Springer, vol. 170(1), pages 1-24, January.
    11. 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.
    12. Akshita Bassi & Aditya Manchanda & Rajwinder Singh & Mahesh Patel, 2023. "A comparative study of machine learning algorithms for the prediction of compressive strength of rice husk ash-based concrete," 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. 118(1), pages 209-238, August.
    13. Amadu, Festus O. & McNamara, Paul E. & Davis, Kristin E., 2021. "Soil health and grain yield impacts of climate resilient agriculture projects: Evidence from southern Malawi," Agricultural Systems, Elsevier, vol. 193(C).
    14. Taihao Wang & Huadong Du & Zezheng Zhao & Zeming Zhou & Ana Russo & Hailing Xi & Jiping Zhang & Chengjun Zhou, 2022. "Prediction of the Impact of Meteorological Conditions on Air Quality during the 2022 Beijing Winter Olympics," Sustainability, MDPI, vol. 14(8), pages 1-13, April.
    15. Zsuzsanna Farkas & Angéla Anda & Gyula Vida & Ottó Veisz & Balázs Varga, 2021. "CO 2 Responses of Winter Wheat, Barley and Oat Cultivars under Optimum and Limited Irrigation," Sustainability, MDPI, vol. 13(17), pages 1-23, September.
    16. Silvia Di Francesco & Stefano Casadei & Ilaria Di Mella & Francesca Giannone, 2022. "The Role of Small Reservoirs in a Water Scarcity Scenario: a Computational Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(3), pages 875-889, February.
    17. 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.
    18. J. Fernando Vera & Rodrigo Macías, 2021. "On the Behaviour of K-Means Clustering of a Dissimilarity Matrix by Means of Full Multidimensional Scaling," Psychometrika, Springer;The Psychometric Society, vol. 86(2), pages 489-513, June.
    19. Daniel Aloise & Pierre Hansen, 2011. "Evaluating a branch-and-bound RLT-based algorithm for minimum sum-of-squares clustering," Journal of Global Optimization, Springer, vol. 49(3), pages 449-465, March.
    20. Wu, Genan & Lu, Xinchen & Zhao, Wei & Cao, Ruochen & Xie, Wenqi & Wang, Liyun & Wang, Qiuhong & Song, Jiexuan & Gao, Shaobo & Li, Shenggong & Hu, Zhongmin, 2023. "The increasing contribution of greening to the terrestrial evapotranspiration in China," Ecological Modelling, Elsevier, vol. 477(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:36:y:2022:i:11:d:10.1007_s11269-022-03204-2. 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.