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Development and Implementation of Support Vector Machine Regression Surrogate Models for Predicting Groundwater Pumping-Induced Saltwater Intrusion into Coastal Aquifers

Author

Listed:
  • Alvin Lal

    (James Cook University)

  • Bithin Datta

    (James Cook University)

Abstract

Predicting the extent of saltwater intrusion (SWI) into coastal aquifers in response to changing pumping patterns is a prerequisite of any groundwater management framework. This study investigates the feasibility of using support vector machine regression (SVMr), an innovative artificial intelligence-based machine learning algorithm for predicting salinity concentrations at selected monitoring wells in an illustrative aquifer under variable groundwater pumping conditions. For evaluation purpose, the prediction results of SVMr are compared with well-established genetic programming (GP) based surrogate models. SVMr and GP models are trained and validated using identical sets of input (pumping) and output (salinity concentration) datasets. The trained and validated models are then used to predict salinity concentrations at specified monitoring wells in response to new pumping datasets. Prediction capabilities of the two learning machines are evaluated using different proficiency measures to ensure their practicality and generalisation ability. The performance evaluation results suggest that the prediction capability of SVMr is superior to GP models. Also, a sensitivity analysis methodology is proposed for assessing the impact of pumping rates on salt concentrations at monitoring locations. This sensitivity analysis provides a subset of most influential pumping rates, which is used to construct new SVMr surrogate models with improved predictive capabilities. The improved prediction capability and the generalisation ability of the SVMr models together with the ability to improve the accuracy of prediction by refining the input set for training makes the use of proposed SVMr models more attractive. Prediction models with more accurate prediction capability makes it potentially very useful for designing large scale coastal aquifer management strategies.

Suggested Citation

  • Alvin Lal & Bithin Datta, 2018. "Development and Implementation of Support Vector Machine Regression Surrogate Models for Predicting Groundwater Pumping-Induced Saltwater Intrusion into Coastal Aquifers," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(7), pages 2405-2419, May.
    • Handle: RePEc:spr:waterr:v:32:y:2018:i:7:d:10.1007_s11269-018-1936-2
    DOI: 10.1007/s11269-018-1936-2
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    References listed on IDEAS

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    1. J. Sreekanth & Bithin Datta, 2011. "Comparative Evaluation of Genetic Programming and Neural Network as Potential Surrogate Models for Coastal Aquifer Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(13), pages 3201-3218, October.
    2. Vasileios Christelis & Aristotelis Mantoglou, 2016. "Pumping Optimization of Coastal Aquifers Assisted by Adaptive Metamodelling Methods and Radial Basis Functions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(15), pages 5845-5859, December.
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    Cited by:

    1. G. Kopsiaftis & V. Christelis & A. Mantoglou, 2019. "Comparison of Sharp Interface to Variable Density Models in Pumping Optimisation of Coastal Aquifers," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(4), pages 1397-1409, March.
    2. 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.
    3. Jina Yin & Frank T.-C. Tsai & Chunhui Lu, 2022. "Bi-objective Extraction-injection Optimization Modeling for Saltwater Intrusion Control Considering Surrogate Model Uncertainty," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(15), pages 6017-6042, December.
    4. Yu, Xiayang & Sreekanth, J. & Cui, Tao & Pickett, Trevor & Xin, Pei, 2021. "Adaptative DNN emulator-enabled multi-objective optimization to manage aquifer−sea flux interactions in a regional coastal aquifer," Agricultural Water Management, Elsevier, vol. 245(C).
    5. Xin Liu & Shunlong Li, 2022. "Impact of COVID-19 pandemic on low-carbon shared traffic scheduling under machine learning model," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(3), pages 987-995, December.
    6. Vasileios Christelis & Aristotelis Mantoglou, 2019. "Pumping Optimization of Coastal Aquifers Using Seawater Intrusion Models of Variable-Fidelity and Evolutionary Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(2), pages 555-568, January.

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