IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v25y2011i4p1143-1152.html
   My bibliography  Save this article

Artificial Neural Network (ANN) Based Modeling for Karstic Groundwater Level Simulation

Author

Listed:
  • Ioannis Trichakis
  • Ioannis Nikolos
  • G. Karatzas

Abstract

A relatively new method of addressing different hydrological problems is the use of artificial neural networks (ANN). In groundwater management ANNs are usually used to predict the hydraulic head at a well location. ANNs can prove to be very useful because, unlike numerical groundwater models, they are very easy to implement in karstic regions without the need of explicit knowledge of the exact flow conduit geometry and they avoid the creation of extremely complex models in the rare cases when all the necessary information is available. With hydrological parameters like rainfall and temperature, as well as with hydrogeological parameters like pumping rates from nearby wells as input, the ANN applies a black box approach and yields the simulated hydraulic head. During the calibration process the network is trained using a set of available field data and its performance is evaluated with a different set. Available measured data from Edward’s aquifer in Texas, USA are used in this work to train and evaluate the proposed ANN. The Edwards Aquifer is a unique groundwater system and one of the most prolific artesian aquifers in the world. The present work focuses on simulation of hydraulic head change at an observation well in the area. The adopted ANN is a classic fully connected multilayer perceptron, with two hidden layers. All input parameters are directly or indirectly connected to the aquatic equilibrium and the ANN is treated as a sophisticated analogue to empirical models of the past. A correlation analysis of the measured data is used to determine the time lag between the current day and the day used for input of the measured rainfall levels. After the calibration process the testing data were used in order to check the ability of the ANN to interpolate or extrapolate in other regions, not used in the training procedure. The results show that there is a need for exact knowledge of pumping from each well in karstic aquifers as it is difficult to simulate the sudden drops and rises, which in this case can be more than 6 ft (approx. 2 m). That aside, the ANN is still a useful way to simulate karstic aquifers that are difficult to be simulated by numerical groundwater models. Copyright Springer Science+Business Media B.V. 2011

Suggested Citation

  • Ioannis Trichakis & Ioannis Nikolos & G. Karatzas, 2011. "Artificial Neural Network (ANN) Based Modeling for Karstic Groundwater Level Simulation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(4), pages 1143-1152, March.
    • Handle: RePEc:spr:waterr:v:25:y:2011:i:4:p:1143-1152
    DOI: 10.1007/s11269-010-9628-6
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11269-010-9628-6
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11269-010-9628-6?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. Purna Nayak & Y. Rao & K. Sudheer, 2006. "Groundwater Level Forecasting in a Shallow Aquifer Using Artificial Neural Network Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 20(1), pages 77-90, February.
    2. Rajib Bhattacharjya & Bithin Datta, 2005. "Optimal Management of Coastal Aquifers Using Linked Simulation Optimization Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 19(3), pages 295-320, June.
    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. Jenq-Tzong Shiau & Hui-Ting Hsu, 2016. "Suitability of ANN-Based Daily Streamflow Extension Models: a Case Study of Gaoping River Basin, Taiwan," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(4), pages 1499-1513, March.
    2. Yashon O. Ouma & Ditiro B. Moalafhi & George Anderson & Boipuso Nkwae & Phillimon Odirile & Bhagabat P. Parida & Jiaguo Qi, 2022. "Dam Water Level Prediction Using Vector AutoRegression, Random Forest Regression and MLP-ANN Models Based on Land-Use and Climate Factors," Sustainability, MDPI, vol. 14(22), pages 1-31, November.
    3. 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.
    4. Jenq-Tzong Shiau & Hui-Ting Hsu, 2016. "Suitability of ANN-Based Daily Streamflow Extension Models: a Case Study of Gaoping River Basin, Taiwan," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(4), pages 1499-1513, March.
    5. L. Karthikeyan & D. Kumar & Didier Graillot & Shishir Gaur, 2013. "Prediction of Ground Water Levels in the Uplands of a Tropical Coastal Riparian Wetland using Artificial Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(3), pages 871-883, February.
    6. Domenico Baú, 2012. "Planning of Groundwater Supply Systems Subject to Uncertainty Using Stochastic Flow Reduced Models and Multi-Objective Evolutionary Optimization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(9), pages 2513-2536, July.
    7. Youngmin Seo & Sungwon Kim & Vijay Singh, 2015. "Estimating Spatial Precipitation Using Regression Kriging and Artificial Neural Network Residual Kriging (RKNNRK) Hybrid Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(7), pages 2189-2204, May.
    8. Shishir Gaur & Sudheer Ch & Didier Graillot & B. Chahar & D. Kumar, 2013. "Application of Artificial Neural Networks and Particle Swarm Optimization for the Management of Groundwater Resources," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(3), pages 927-941, February.
    9. 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.
    10. Hamid Safavi & Mahdieh Esmikhani, 2013. "Conjunctive Use of Surface Water and Groundwater: Application of Support Vector Machines (SVMs) and Genetic Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(7), pages 2623-2644, May.
    11. 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.

    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. Hamid Safavi & Mahdieh Esmikhani, 2013. "Conjunctive Use of Surface Water and Groundwater: Application of Support Vector Machines (SVMs) and Genetic Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(7), pages 2623-2644, May.
    2. 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.
    3. Fateme Heydari & Bahram Saghafian & Majid Delavar, 2016. "Coupled Quantity-Quality Simulation-Optimization Model for Conjunctive Surface-Groundwater Use," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(12), pages 4381-4397, September.
    4. Om Prakash Vats & Bhrigumani Sharma & Juergen Stamm & Rajib Kumar Bhattacharjya, 2020. "Groundwater Circulation Well for Controlling Saltwater Intrusion in Coastal aquifers: Numerical study with Experimental Validation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(11), pages 3551-3563, September.
    5. Chattopadhyay, Pallavi Banerjee & Rangarajan, R., 2014. "Application of ANN in sketching spatial nonlinearity of unconfined aquifer in agricultural basin," Agricultural Water Management, Elsevier, vol. 133(C), pages 81-91.
    6. 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.
    7. 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.
    8. Raymond Kim & Daniel Loucks & Jery Stedinger, 2012. "Artificial Neural Network Models of Watershed Nutrient Loading," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(10), pages 2781-2797, August.
    9. L. Karthikeyan & D. Kumar & Didier Graillot & Shishir Gaur, 2013. "Prediction of Ground Water Levels in the Uplands of a Tropical Coastal Riparian Wetland using Artificial Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(3), pages 871-883, February.
    10. Desalegn Edossa & Mukand Babel, 2011. "Application of ANN-Based Streamflow Forecasting Model for Agricultural Water Management in the Awash River Basin, Ethiopia," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(6), pages 1759-1773, April.
    11. 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.
    12. Singh, Ajay, 2014. "Simulation–optimization modeling for conjunctive water use management," Agricultural Water Management, Elsevier, vol. 141(C), pages 23-29.
    13. A. Izady & K. Davary & A. Alizadeh & A. Moghaddam Nia & A. Ziaei & S. Hasheminia, 2013. "Application of NN-ARX Model to Predict Groundwater Levels in the Neishaboor Plain, Iran," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(14), pages 4773-4794, November.
    14. Alvin Lal & Bithin Datta, 2019. "Application of Monitoring Network Design and Feedback Information for Adaptive Management of Coastal Groundwater Resources," IJERPH, MDPI, vol. 16(22), pages 1-26, November.
    15. Ozgur Kisi & Meysam Alizamir & Mohammad Zounemat-Kermani, 2017. "Modeling groundwater fluctuations by three different evolutionary neural network techniques using hydroclimatic data," 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. 87(1), pages 367-381, May.
    16. Thiago Victor Medeiros Nascimento & Celso Augusto Guimarães Santos & Camilo Allyson Simões Farias & Richarde Marques Silva, 2022. "Monthly Streamflow Modeling Based on Self-Organizing Maps and Satellite-Estimated Rainfall Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(7), pages 2359-2377, May.
    17. Sunayana & Komal Kalawapudi & Ojaswikrishna Dube & Renuka Sharma, 2020. "Use of neural networks and spatial interpolation to predict groundwater quality," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(4), pages 2801-2816, April.
    18. 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.
    19. Rakesh Kumar & Narendra Goel & Chandranath Chatterjee & Purna Nayak, 2015. "Regional Flood Frequency Analysis using Soft Computing Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(6), pages 1965-1978, April.
    20. 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.

    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:25:y:2011:i:4:p:1143-1152. 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.