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Forecasting Aquifer Parameters Using Artificial Neural Networks

Volume 9, Issue 5, 2006, pp. 429-444
DOI: 10.1615/JPorMedia.v9.i5.40
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ABSTRACT

This study proposes an artificial neural network (ANN) model to solve an inverse parameter identification problem for groundwater modeling. It is a problem for which the transmissivities can be obtained for given hydraulic heads. ANN may be a useful tool for parameter estimation problems because of its ability to model complex nonlinear relationships between state variables and system parameters without a priori assumptions of the nature of a relationship like a black box. To carry out a parameter estimation using the ANN, a hypothetical example has been examined under two scenarios, one involving the sink and/or source terms, the second without these. In the ANN model, the network is trained for about 5, 10, and 20% of all data, and then transmissivities in the other cells are forecasted. Results show that observed and forecasted transmissivities are in good agreement when about 10 and 20% of the hydraulic heads in the solution domain are known.

CITED BY
  1. Karahan Halil, Ayvaz M. Tamer, Simultaneous parameter identification of a heterogeneous aquifer system using artificial neural networks, Hydrogeology Journal, 16, 5, 2008. Crossref

  2. Lin Hsien-Tsung, Ke Kai-Yuan, Chen Chu-Hui, Wu Shih-Ching, Tan Yih-Chi, Estimating anisotropic aquifer parameters by artificial neural networks, Hydrological Processes, 24, 22, 2010. Crossref

  3. Ayvaz M. Tamer, Karahan Halil, Aral Mustafa M., Aquifer parameter and zone structure estimation using kernel-based fuzzy c-means clustering and genetic algorithm, Journal of Hydrology, 343, 3-4, 2007. Crossref

  4. Ayvaz M. Tamer, Simultaneous determination of aquifer parameters and zone structures with fuzzy c-means clustering and meta-heuristic harmony search algorithm, Advances in Water Resources, 30, 11, 2007. Crossref

  5. Yu Guo, Fang Yizhu, Li Haitao, Wang Chenyu, Zhang Dongming, Establishment and application of prediction model of natural gas reserve and production in Sichuan Basin, Journal of Petroleum Exploration and Production Technology, 11, 6, 2021. Crossref

  6. Li Haitao, Yu Guo, Fang Yizhu, Chen Yanru, Wang Chenyu, Zhang Dongming, Studies on natural gas reserves multi-cycle growth law in Sichuan Basin based on multi-peak identification and peak parameter prediction, Journal of Petroleum Exploration and Production Technology, 11, 8, 2021. Crossref

  7. Eryiğit Miraç, Estimation of parameters in groundwater modelling by modified Clonalg, Journal of Hydroinformatics, 23, 2, 2021. Crossref

  8. Maaoui Walaeddine , Lazhar Ramzi , Najjari Mustapha , SOIL MOISTURE RETRIEVAL MODEL BASED ON DIELECTRIC MEASUREMENTS AND ARTIFICIAL NEURAL NETWORK , Journal of Porous Media, 25, 8, 2022. Crossref

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