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Revealing the background of groundwater level dynamics: Contributing factors, complex modeling and engineering applications

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  • Kostić, Srđan
  • Stojković, Milan
  • Guranov, Iva
  • Vasović, Nebojša

Abstract

Authors provide extensive analysis of groundwater level dynamics, in order to classify the observed data, to determine the role of contributing factors and to derive specific estimation models, based on the recordings made at four piezometric stations in Serbia: Leskovac, Kruševac, Negotin and Bogatić. Results obtained indicate the predominant impact of noise confirmed by surrogate data testing, determinism test and Box–Jenkins approach. It is shown that surface water level has a strong impact on the observed dynamics, while the effect of rainfall is almost statistically insignificant. Authors also propose different estimation models: as a combination of deterministic and stochastic components, as a nonlinear function of contributing factors and in a form of system of stochastic differential equations. At the end, authors illustrate simple application of some of derived models in engineering practice for slope stability analyzes.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:chsofr:v:127:y:2019:i:c:p:408-421
    DOI: 10.1016/j.chaos.2019.07.007
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Milan Stojković & Srđan Kostić & Stevan Prohaska & Jasna Plavšić & Vesna Tripković, 2017. "A New Approach for Trend Assessment of Annual Streamflows: a Case Study of Hydropower Plants in Serbia," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(4), pages 1089-1103, March.
    4. 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.
    5. Kostić, Srđan & Vasović, Nebojša & Perc, Matjaž & Toljić, Marinko & Nikolić, Dobrica, 2013. "Stochastic nature of earthquake ground motion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(18), pages 4134-4145.
    6. 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.
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