Modeling daily discharge responses of a large karstic aquifer using soft computing methods: Artificial neural network and neuro-fuzzy

Journal of Hydrology

Volumn 381, Issue 1-2, 2010, Pages 101-111

  Kurtulus, Bedri ^a,b   Razack, Moumtaz ^a  

^a UNIVERSITÉ DE POITIERS   (France)

^b HACETTEPE UNIVERSITY   (Turkey)

Author keywords

Adaptive Neuro Fuzzy Interface System (ANFIS); Artificial Neural Networks (ANN); Karst; La Rochefoucauld aquifer; Modeling; Soft computing

Indexed keywords

ANFIS MODEL; ARTIFICIAL NEURAL NETWORK; ARTIFICIAL NEURAL NETWORKS (ANN); AUTOMATIC PROCEDURES; CALIBRATED MODEL; DAILY DISCHARGE; GENERALIZATION CAPABILITY; HYDROGRAPHS; INPUT MODELS; KARST; KARST AQUIFER; KARST SYSTEM; KARSTIC AQUIFER; MAIN COMPONENT; MULTIPLE INPUTS; NEURO-FUZZY; NON-LINEAR; PEAK DISCHARGE; PEAK FLOWS; PIEZOMETRIC LEVELS; ROOT MEAN SQUARE ERRORS; SIMULATED DISCHARGES; SOFT COMPUTING METHODS; SUMMARY STATISTIC;

AQUIFERS; FORECASTING; FUZZY LOGIC; FUZZY SYSTEMS; HIERARCHICAL SYSTEMS; HYDROGEOLOGY; INTERACTIVE COMPUTER SYSTEMS; LANDFORMS; LINEAR SYSTEMS; MATLAB; NONLINEAR SYSTEMS; SOFT COMPUTING; WATER SUPPLY;

FUZZY NEURAL NETWORKS;

AQUIFER; ARTIFICIAL NEURAL NETWORK; DISCHARGE; FUZZY MATHEMATICS; GROUNDWATER FLOW; KARST HYDROLOGY; PERFORMANCE ASSESSMENT;

FRANCE;

EID: 73649113119     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhydrol.2009.11.029     Document Type: Article

References (62)

1. Akil M., Kita I., Yano A., and Nishiyama S. A comparative study of artificial neural networks and neuro-fuzzy in continuous modeling of the daily and hourly behavior of runoff. Journal of Hydrology 337 1-2 (2007) 22-34
2. Bakalowicz M. Karst groundwater: a challenge for new resources. Hydrogeology Journal 13 1 (2005) 148-160
3. Baratti M. River flow forecast for reservoir management through neural networks. Neurocomputing 55 (2003) 421-437
4. Bardossy A., Bronsterts A., and Merz B. 1-, 2- and 3-dimensional modelling of water movement in the unsaturated soil matrix a using fuzzy approach. Advances in Water Resources 18 4 (1996) 237-251
5. Beale R., and Jackson T. Neural Computing: An Introduction (1991), Adam Hilger, Techno House, Bristol, UK
6. Bowden G., Dandy G., and Maier H. Input determination for neural network model in water resources applications. Part 1 - background ans methodology. Journal of Hydrology 301 (2005) 75-92
7. Chang F.J., and Chang Y.T. Adaptive neuro-fuzzy inference system for prediction of water level in reservoir. Advances in Water Resources 29 (2006) 1-10
8. Chang N.B., Weu G.G., and Chen Y.L. A fuzzy multi-objective programming approach for optimal management of the reservoir watershed. European Journal of Operational Research 99 2-1 (1997) 289-302
9. Chen S.H., Lin Y.H., Chang L.C., and Chang F.J. The strategy of building a flood forecast model by neuro fuzzy network. Hydrological Processes 20 (2006) 1525-1540
10. Cigizoglu H.K. Generalized regression neural network in monthly flow forecasting. Civil Engineering and Environmental Systems 22 2 (2005) 71-84
11. Demuth H., and Beale M. 'Neural Networks Toolbox' User Guide (2003), Mathworks Inc., Natick, MA, USA
12. Denic-Jukic V., and Jukic D. Composite transfer functions for karst aquifers. Journal of Hydrology 274 (2003) 80-94
13. Dixon B. Applicability of neuro-fuzzy techniques in predicting ground water vulnerability: a sensitivity analysis. Journal of Hydrology 309 (2005) 17-38
14. Doerfliger N., Jeannin P.Y., and Zwahlen F. Water vulnerability assessment in karst environments: a new method of defining protection areas using a multi-attribute approach and GIS tools (EPIK method). Environmental Geology 39 2 (1999) 165-176
15. Dou C., Woldt W., Dahab M., and Bogardi I. Transient groundwater flow simulation using a fuzzy set approach. Ground Water 35 2 (1997) 205-215
16. Dou C., Woldt W., Bogardi I., and Dahab M. Numerical solute transport simulation using fuzzy set approach. Journal of Contaminant Hydrology 27 1-2 (1997) 107-126
17. Drogue C. Structure de certains aquifères karstiques d'après les résultats de travaux de forage. Comptes Rendus al'Académie des Sciences, Paris, Série III 278 (1974) 2621-2624
18. Einsield F. Flow system dynamics and water storage of a fissured-porous karst aquifer characterized by artificial and environmental tracers. Journal of Hydrology 279 (2005) 312-321
19. Fausett L. Fundamentals of Neural Networks: Architecture, Algorithms and Application (1994), Prentice Hall Int., Inc., USA
20. Fleury P., Plagnes V., and Bakalowicz M. Modelling of the functioning of karst aquifers with a reservoir model: application to Fontaine de Vaucluse, South of France. Journal of Hydrology 345 (2007) 38-49
21. French M., Krajewski W., and Cuykendall R. Rainfall forecasting in space and time using a neural network. Journal of Hydrology 137 (1992) 1-31
22. Goswami M., and O'Connor K.M. Real-time flow forecasting in the absence of quantitative precipitation forecasts: a multi-model approach. Journal of Hydrology 334 1-2 (2007) 20 125-145
23. Gwo-Fong L., and Lu-Hsien C. A non-linear rainfall-runoff model using radial basis function network. Journal of Hydrology 289 (2004) 1-8
24. Hagan M.T., and Menhaj M.B. Training feedforward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks 5 (1994) 989-993
25. Hagan M.T., Demuth H.B., and Beale M.H. Neural Network Design (1996), PWS, Boston, MA
26. Halff A.H., Halff H.M., and Azmoodeh M. Predicting runoff from rainfall using neural networks. ASCE Proceedings of Engineering Hydrology (1993) 760-765
27. Hsu K., Gupta H.V., and Sorooshian S. Artificial neural network modeling of the rainfall-runoff process. Water Resources Research 31 10 (1995) 2517-2530
28. Imrie C.E., Durucan S., and Korre A. River flow prediction using artificial neural networks: generalization beyond the calibration range. Journal of Hydrology 233 (2000) 138-153
29. Jang J.S.R. Self-learning fuzzy controllers based on temporal back propagation. IEEE Transactions on Neural Networks 3 5 (1992) 714-723
30. Jang J.S.R. ANFIS adaptive-network-based fuzzy inference systems. IEEE Transactions on Systems, Man and Cybernetics 23 03 (1993) 665-685
31. Jang J.S.R. Neuro-Fuzzy and Soft Computing (1997), Prentice-Hall, New Jersey
32. Kaufman G. A model comparison of karst aquifer evolution for different matrix-flow formulations. Journal of Hydrology 283 (2003) 281-289
33. Kurtulus B., and Razack M. Evaluation of the ability of an artificial neural network model to simulate the input-output responses of a large karstic aquifer: the La Rochefoucauld aquifer, Charente, France. Hydrogeology Journal 15 (2007) 241-254
34. Lallahem S., and Mania J. A non-linear rainfall-runoff model using neural network technique: example in fractured porous media. Mathematical and Computer Modeling 37 (2003) 1047-1061
35. Larocque M., and Razack M. Synthèse hydrogéologique de l'aquifère de La Rochefoucauld: bilan des nouvelles connaissances (hydrogeological synthesis of La Rochefoucauld karst aquifer: review of new data). Hydrogéologie 3 (1998) 35-45
36. Lee E.S., and Krothe N.C. A four component mixing model for water in a karst terrain in south-central Indiana, USA. Using solute concentration and stable isotopes as tracers. Chemical Geology 179 (2001) 129-143
37. Lu R.S., and Lo S.L. Diagnosing reservoir water quality using self organising maps and fuzzy theory. Water Resources 36 9 (2002) 2265-2274
38. Mamdani E.H., and Assilian S. An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies 7 1 (1974) 1-13
39. Mangin, A., 1975. Contribution à l'étude hydrodynamique des aquifères karstiques. Phd thesis, University of Dijon, France.
40. Maren A.J. Neural networks structures. In: Maren A.J., Harston C.T., and Pap R.M. (Eds). Handbook of Neural Computing and Applications (1990), Academic Press Inc., USA
41. Minns A.W., and Hall M.J. Artificial neural networks as rainfall runoff models. Hydrological Sciences Journal 41 3 (1996) 399-417
42. Moré J.J. The Levenberg-Marquardt algorithm: implementation and theory. In: Watson G.A. (Ed). Lecture Notes in Mathematics vol. 630 (1978), Springer Verlag, Berlin-Heidelberg-New York 105-116
43. Nauck D., and Kruse R. Obtaining interpretable fuzzy classification rules from medical data. Artificial Intelligence in Medicine 16 2 (1999) 149-169
44. Nguyen H.T., Prasad N.R., Walker C.L., and Walker E.A. A First Course in Fuzzy and Neural Control (2003), Chapman & Hall/CRC Press, New York
45. Ozelkan E.C., and Duckstein L. Fuzzy conceptual rainfall-runoff models. Journal of Hydrology 253 1-4 (2001) 41-68
46. Özgür K. Suspended sediment estimation using neuro-fuzzy and neural network approaches. Hydrological Sciences Journal 50 4 (2006) 683-696
47. Press W.H., Teukolski S.A., Vetterling W.T., and Flannery B.P. Numerical Recipes in Fortran. second ed. (1992), Cambridge Univ. Press, New York
48. Rajurkar M.P., Kothyari U.C., and Chaube U.C. Modeling of the daily rainfall-runoff relationship with artificial neural network. Journal of Hydrology 285 (2004) 96-113
49. Sajikumar N., and Thandaveswara B.S. A nonlinear rainfall-runoff model using an artificial neural network. Journal of Hydrology 216 (1999) 32-55
50. Schulz K., and Huwe B. Water flow modeling in the unsaturated zone with imprecise parameters using a fuzzy approach. Journal of Hydrology 201 (1997) 211-229
51. Shamseldin A.Y. Application of a neural technique to rainfall-runoff modelling. Journal of Hydrology 199 (1997) 272-294
52. Shamseldin A.Y., O'Connor K.M., and Liang G.C. Methods for combining the outputs of different rainfall runoff models. Journal of Hydrology 199 (1997) 203-229
53. Silva I.N., Arruda L.V.R., and Amaral W.C. A novel approach to robust parameter estimation using neuro fuzzy systems. Mathematics and Computers in Simulation 48 (1999) 251-268
54. Takagi T., and Sugeno M. Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems Man and Cybernetics 15 1 (1985) 116-132
55. Task Committee A.S.C.E. Artificial neural networks in hydrology-I: preliminary concepts. Journal of Hydrologic Engineering 5 2 (2000) 115-123
56. Taylor, C., Greene, E, 2001. Quantitative Approaches in Characterizing Karst Aquifers. USGS Karst Int. Gr. Proc., Water-Resources Investigations Report 01-4011, 164-166.
57. Tutmez B., Hatipoglu Z., and Kaymak U. Modelling electrical conductivity of groundwater using an adaptive neuro-fuzzy inference system. Computers & Geosciences 32 (2006) 421-433
58. Xiong L., Asaad Y., Shamseldin Y., and O'Connor K.M. A non-linear combination of the forecasts of rainfall-runoff models by the first order Takagi-Sugeno fuzzy system. Journal of Hydrology 245 1-4 (2001) 196-217
59. Yarar A., Onucyildiz M., and Copty N. Modelling level changes in lakes using neuro-fuzzy and artificial neural networks. Journal of Hydrology 365 (2009) 329-334
60. Yu P.S., and Yang T.C. Fuzzy multi-objective function for rainfall runoff model calibration. Journal of Hydrology 238 1-2 (2000) 1-14
61. Zadeh L.A. Fuzzy sets. Information and Control 8 (1965) 338-353
62. Zhu A.X., and Mackay D.S. Effects of spatial detail of soil information on watershed modeling. Journal of Hydrology 248 1-4 (2001) 54-77