COMPARISON OF ANFIS AND ORDINARY KRIGING TO ASSESS HYDRAULIC HEAD DISTRIBUTION - The Orgeval Case Study

Bedri Kurtulus, Nicolas Flipo, Patrick Goblet, Guillaume Vilain, Julien Tournebize, Gaëlle Tallec

Abstract

In this study, two methods are evaluated for assessing hydraulic head distribution in an aquifer unit. These methods consist in Ordinary Kriging (OK) and Adaptive Neuro Fuzzy based Inference System (ANFIS). Both methods are applied on the same case study: a part of the agricultural basin of the Orgeval located 70 km east of Paris, France. 68 samples were used to predict hydraulic head distribution on a 100 m square - grid. Cartesian coordinates of the samples were used as inputs of the ANFIS, which gives encouraging result. Both simulations have realistic pattern (R2 > 0.97) even if OK performs slightly better than ANFIS at sampling site. Simulated hydraulic head distributions present discrepancies because the two methods capture different patterns. Combined use of the two approaches allow for improving the sampling location of the observation network.

References

  1. ASCE Task Committee, 2000, Artificial neural networks in hydrology-I: Preliminary concepts, J. Hydrol. Eng. 5(2): 115-123
  2. Anctil, F., Filion, M. and Tournebize, J., 2009. A neural network experiment on the simulation of daily nitratenitrogen and suspended sediment fluxes from a small agricultural catchments. Ecol Model 220: 879-887.
  3. Chilès, J.-P., Delfiner, P., 1999. Geostatistics: modeling spatial uncertainty.Wiley, New York.
  4. Cigizoglu, H.K., 2005, Generalized regression neural network in monthly flow forecasting. Civil Engineering and Environmental Systems, 22(2):71-84.
  5. Dacharry, M., 1993. Encyclopédie AXIS.
  6. Demuth H and Beale M., 2003, 'Neural networks toolbox' user guide. Mathworks Inc., Natick, MA., U.S.A.
  7. Firat, M., Gungor, M., 2007, River flow estimation using adaptive neuro fuzzy inference system Mathematics and Computers in Simulation. 75: 87-96.
  8. Flipo, N., Jeannée, N., Poulin, M., Even, S., Ledoux, E., 2007a, Assessment of nitrate pollution in the Grand Morin aquifers (France): Combined use of geostatistics and physically based modelling. Environmental Pollution 146, 241-256. doi:10.1016/ j.envpol.2006.03.056.
  9. Flipo, N., Even, S., Poulin, M., Théry, S., Ledoux, E., 2007b, Modeling nitrate fluxes at the catchment scale using the integrated tool CAWAQS. Sci Total Environ. 375, 69-79.
  10. Geovariances 2008, Isatis Technical References, version 8, 148 p.
  11. Goovaerts, P., 1997. Geostatistics for Natural Ressources Evaluation. Oxford University Press, New York, 181 pp.
  12. Isaaks, E., Srivastava, R., 1989. An Introduction to Applied Geostatistics. Oxford University Press.
  13. Jang, J.S.R., 1992, Self-learning fuzzy controllers based on temporal back propagation, IEEE Trans. Neural Networks. 3 (5) 714-723
  14. Jang, J.S.R.., 1993, ANFIS adaptive-network-based fuzzy inference systems, IEEE Trans. Systems, Man Cybern. 23 (03) 665-685
  15. Jang, J. S. R., 1997, Neuro-Fuzzy and Soft Computing, Prentice-Hall, New Jersey.
  16. Johannet, A., Ayral, P.A., Vayssade, B., 2007, Modelling non measurable processes by neural network: Forecasting underground flow - Case study of the Cèze Basin (Gard - France). Advances and Innovations in Systems, computing sciences and software engineering. 53-58
  17. Kholghi, M., Hosseini, S.M., 2008, Comparison of Groundwater Level Estimation using Neuro-fuzzy and Ordinary Kriging. Environmental Modeling and Assessment doi - 10.1007/s10666-008-9174-2
  18. Kurtulus B., Razack M., 2007, Evaluation of the ability of an artificial neural network model to simulate the input-output responses of a large karstic aquifer. The La Rochefoucauld (Charente, France), Hydro-geology Journal (15) 2:241-254.
  19. Kurtulus, B., 2008, Modelling of Groundwater Flow and Quality in Karstic Systems Using Soft Computing Methods (Neural Networks, Fuzzy Logic, Ph.D. Thesis Hacettepe Univeristy Institute of Graduate Studies in Science and Engineering and Poitiers University, Faculty of Fundamental and Applied Science, Ankara-Poitiers, 163p.
  20. Özgür, K., 2006, Suspended sediment estimation using neuro-fuzzy and neural network approaches Hydrological Sciences. 50(4): 683-696.
  21. Pardo-Iguzquiza, E., Chica-Olmo, M., Jose GarciaSoldado, M., Luque-Espinar, J. A., 2009, Using Semivariogram Parameter Uncertainty in Hydrogeological Applications. Ground Water 47(1), 25-34.
  22. Pratihar, D. K., 2008, Soft Computing. Alpha Science International Ltd. 229p.
  23. Renard, F., Jeannée, N., 2008, Estimating transmissivity fields and their influence on flow and transport: The case of Champagne mounts. WRR 44, W11414, doi:10.1029/2008WR007033.
  24. Rivest, M., Marcotte, D., Pasquier, P., 2008,Hydraulic head field estimation using kriging with an external drift: A way to consider conceptual model information. Journal of Hydrology 361, 349- 361
  25. Takagi, T., Sugeno, M., 1985, Fuzzy identification of systems and Its applications to modeling and control, IEEE Trans. Systems Man and Cybernetics 15(1): 116-132
  26. Zadeh, L.A., 1965, Fuzzy sets. Information and Control. 8:338-353
  27. Zadeh L.A., 2008, Is there a need for fuzzy logic? Information Sciences. 178: 2751-2779
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Paper Citation


in Harvard Style

Kurtulus B., Flipo N., Goblet P., Vilain G., Tournebize J. and Tallec G. (2009). COMPARISON OF ANFIS AND ORDINARY KRIGING TO ASSESS HYDRAULIC HEAD DISTRIBUTION - The Orgeval Case Study . In Proceedings of the International Joint Conference on Computational Intelligence - Volume 1: ICNC, (IJCCI 2009) ISBN 978-989-674-014-6, pages 371-378. DOI: 10.5220/0002319903710378


in Bibtex Style

@conference{icnc09,
author={Bedri Kurtulus and Nicolas Flipo and Patrick Goblet and Guillaume Vilain and Julien Tournebize and Gaëlle Tallec},
title={COMPARISON OF ANFIS AND ORDINARY KRIGING TO ASSESS HYDRAULIC HEAD DISTRIBUTION - The Orgeval Case Study},
booktitle={Proceedings of the International Joint Conference on Computational Intelligence - Volume 1: ICNC, (IJCCI 2009)},
year={2009},
pages={371-378},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002319903710378},
isbn={978-989-674-014-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Joint Conference on Computational Intelligence - Volume 1: ICNC, (IJCCI 2009)
TI - COMPARISON OF ANFIS AND ORDINARY KRIGING TO ASSESS HYDRAULIC HEAD DISTRIBUTION - The Orgeval Case Study
SN - 978-989-674-014-6
AU - Kurtulus B.
AU - Flipo N.
AU - Goblet P.
AU - Vilain G.
AU - Tournebize J.
AU - Tallec G.
PY - 2009
SP - 371
EP - 378
DO - 10.5220/0002319903710378