Hydraulic head interpolation using anfis-model selection and sensitivity analysis

Computers and Geosciences

Volumn 38, Issue 1, 2012, Pages 43-51

  Kurtulus, Bedri ^a,b   Flipo, Nicolas ^a  

^a PSL RESEARCH UNIVERSITY   (France)

^b MUGLA SITKI KOCMAN UNIVERSITY   (Turkey)

Author keywords

Anfis; Hydraulic head; Hydrogeology; Sensitivity analysis; Spatial interpolation

Indexed keywords

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM; AGRICULTURAL WATERSHEDS; ANFIS; ANFIS MODEL; BEST MODEL; CARTESIAN COORDINATE; DATA SETS; ERROR CELLS; ERROR PROPAGATION; GAUSSIANS; HYDRAULIC HEAD; HYDRAULIC HEADS; INPUT NODE; PERFORMANCE CRITERION; SELECTION METHODS; SENSIBILITY ANALYSIS; SPATIAL INTERPOLATION;

AQUIFERS; CELLS; FUZZY INFERENCE; GEOLOGIC MODELS; HYDROGEOLOGY; INTERPOLATION; MEMBERSHIP FUNCTIONS; SOILS; STATISTICAL TESTS;

SENSITIVITY ANALYSIS;

DATA SET; ERROR ANALYSIS; HYDRAULIC HEAD; HYDROGEOLOGY; INTERPOLATION; MODELING; SENSITIVITY ANALYSIS; WATER FLOW; WATERSHED;

FRANCE; SEINE BASIN;

EID: 82455208909     PISSN: 00983004     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cageo.2011.04.019     Document Type: Article

References (70)

1. Abedini M., Nasseri M., Ansari A. Cluster-based ordinary kriging of piezometric head in west Texas/New Mexico-testing of hypothesis. Journal of Hydrology 2008, 351(3-4):360-367.
2. Abrahart, R., Kneale, P., See, L. (Eds.), 2004. Neural Networks for Hydrological Modelling. A.A. Balkema Publishers.
3. Ahmadi S., Sedghamiz A. Application and evaluation of kriging and cokriging methods on groundwater depth mapping. Environmental Monitoring and Assessment 2008, 138(1):357-368.
4. Anctil F., Filion M., Tournebize J. A neural network experiment on the simulation of daily nitrate-nitrogen and suspended sediment fluxes from a small agricultural catchment. Ecological Modeling 2009, 220:879-887.
5. Ayvaz M.T., Karahan H., Aral M.M. Aquifer parameter and zone structure estimation using kernel-based fuzzy c-means clustering and genetic algorithm. Journal of Hydrology 2007, 343:240-253.
6. Bárdossy A. The use of fuzzy rules for the description of elements of the hydrological cycle. Ecological Modeling 1996, 85:59-65.
7. Bárdossy A., Haberlandt U., Krysanova V. Automatic fuzzy-rule assessment and its application to the modelling of nitrogen leaching for large regions. Soft Computing 2003, 7:370-385.
8. Beven K. Changing ideas in hydrology. The case of physically-based model. Journal of Hydrology 1989, 105:157-172.
9. Billen G., Garnier J., Mouchel J.-M., Silvestre M. The Seine system: introduction to a multidisciplinary approach of the functioning of a regional river system. Science of the Total Environment 2007, 375:1-12.
10. Boezio M., Costa J., Koppe J. Accounting for extensive secondary information to improve watertable mapping. Natural Resources Research 2006, 15(1):33-48.
11. Brochu Y., Marcotte D. A simple approach to account for radial flow and boundary conditions when kriging hydraulic head fields for confined aquifers. Mathematical Geology 2003, 35(2):111-139.
12. Buchanan S., Triantafilis J. Mapping water table depth using geophysical and environmental variables. Ground Water 2009, 47(1):80-96.
13. Chang F.-J., Chang Y.-T. Adaptive neuro-fuzzy inference system for prediction of water level in reservoir. Advances in Water Research 2006, 29:1-10.
14. Chang Y., Chang L.-C., Chang F.-J. Intelligent control for modeling of real-time reservoir operation. Part II. Artificial neural network with operating rule curves. Hydrological Processes 2005, 19:1431-1444.
15. Chen S.-H., Lin Y.-H., Chang L.-C., Chang F.-J. The strategy of building a flood forecast model by neuro-fuzzy network. Hydrological Processes 2006, 20:1525-1540.
16. Chidthong Y., Tanaka H., Supharatid S. Developing a hybrid multi-model for peak flood forecasting. Hydrological Processes 2009, 23:1725-1738.
17. Chu H.-J., Chang L.-C. Application of optimal control and fuzzy theory for dynamic groundwater remediation design. Water Resource Management 2009, 23:647-660.
18. Demuth, H., Beale, M., 2003. Neural Networks Toolbox User Guide. Technical Report, Mathworks Inc.
19. Desbarats A.J., Logan C.E., Hinton M.J., Sharpe D.R. On the kriging of water table elevations using collateral information from a digital elevation model. Journal of Hydrology 2002, 255(1-4):25-38.
20. Ebel B., Loague K. Physics-based hydrologic-response simulation: seeing through the fog of equifinality. Hydrological Processes 2006, 20:2887-2900.
21. El-Shafie A., Taha M.R., Noureldin A. A neuro-fuzzy model for inflow forecasting of the Nile River at Aswan High Dam. Water Resource Management 2007, 21:533-556.
22. Firat M. Comparison of artificial intelligence techniques for river flow forecasting. Hydrological Earth System Science 2008, 12:123-138.
23. Firat M., Gungor M. River flow estimation using adaptive neuro fuzzy inference system. Mathematics and Computers in Simulation 2007, 75:87-96.
24. Firat M., Gungor M. Hydrological time-series modelling using an adaptive neuro-fuzzy inference system. Hydrological Processes 2008, 22:2122-2132.
25. Firat M., Turan M.E., Yurdusev M.A. Comparative analysis of fuzzy inference systems for water consumption time series prediction. Journal of Hydrology 2009, 374:235-241.
26. Flipo N., Even S., Poulin M., Théry S., Ledoux E. Modelling nitrate fluxes at the catchment scale using the integrated tool CaWaQS. Science of the Total Environment 2007, 375:69-79.
27. Flipo N., Jeannée N., Poulin M., Even S., Ledoux E. Assessment of nitrate pollution in the Grand Morin aquifers (France): Combined use of geostatistics and physically-based modeling. Environmental Pollution 2007, 146(1):241-256.
28. Flipo N., Rabouille C., Poulin M., Even S., Tusseau-Vuillemin M., Lalande M. Primary production in headwater streams of the Seine basin: The Grand Morin case study. Science of the Total Environment 2007, 375:98-109.
29. Flipo, N., Rejiba, F., Kurtulus, B., Tournebize, J., Tallec, G., Vilain, G., Garnier, J., Ansart, P., Lotteau, M., 2009. Caractérisation des fonctionnements hydrologique et hydrogéologique du bassin de l'Orgeval. Technical Report, PIREN Seine.
30. Garcia L.A., Shigidib A. Using neural networks for parameter estimation in ground water. Journal of Hydrology 2006, 318:215-231.
31. Heuvelmans G., Muys B., Feyen J. Regionalisation of the parameters of a hydrological model: Comparison of linear regression models with artificial neural nets. Journal of Hydrology 2006, 319(1-4):245-265.
32. Hoeksema R., Clapp R., Thomas A., Hunley A., Farrow N., Dearstone K. Cokriging model for estimation of water table estimation. Water Resources Research 1989, 25(3):429-438.
33. Hong Y.-S.T., White P.A. Hydrological modeling using a dynamic neuro-fuzzy system with on-line and local learning algorithm. Advances in Water Research 2009, 32:110-119.
34. Jang J. ANFIS adaptive-network-based fuzzy inference systems. IEEE Transactions on Systems, Man, and Cybernetics 1993, 23(3):665-685.
35. Jang J. Neuro-fuzzy modeling and control. Proceedings of the IEEE 1995, 83(3):378-406.
36. Jang J. Input selection for anfis learning. IEEE International Conference on Fuzzy Systems 1996, 2:1493-1499.
37. Johannet, A., Ayral, P., Vayssade, B., 2007. Modelling non measurable processes by neural networks: Forecasting underground flow case study of the Cèze basin (Gard-France). In: Advances and Innovation in Systems, Computing Sciences and Software Engineering, pp. 53-58.
38. Karahan H., Ayvaz M.T. Simultaneous parameter identification of a heterogeneous aquifer system using artificial neural networks. Hydrogeology Journal 2008, 16:817-827.
39. Kholghi M., Hosseini S.M. Comparison of groundwater level estimation using neuro-fuzzy and ordinary kriging. Environmental Model Assessment 2009, 14(6):729-737.
40. Kuo Y.-M., Liu C.-W., Lin K.-H. Evaluation of the ability of an artificial neural network model to assess the variation of groundwater quality in an area of blackfoot disease in Taiwan. Water Research 2004, 38(1):148-158.
41. Kurtulus B., Flipo N., Goblet P. Sensitivity analysis on an adaptative neuro fuzzy inference system (anfis) for hydraulic head interpolation: Orgeval experimental site/france. XVIII International Conference on Water Resources CMWR 2010 2010, J. Carrera (Ed.).
42. Kurtulus B., Flipo N., Goblet P., Vilain G., Tournebize J., Tallec G. Hydraulic head interpolation in an aquifer unit using ANFIS and Ordinary Kriging. Studies in Computational Intelligence 2011, 343:265-276,. doi:10.1007/978-3-642-20206-3_18.
43. Kurtulus B., 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 (Charente France). Hydrogeology Journal 2007, 15(2):241-254.
44. Kurtulus B., Razack M. Modeling daily discharge responses of a large karstic aquifer using soft computing methods: Artificial neural network and neuro-fuzzy. Journal of Hydrology 2010, 381:101-111.
45. Ledoux E., Gomez E., Monget J., Viavattene C., Viennot P., Ducharne A., Benoit M., Mignolet C., Schott C., Mary B. Agriculture and groundwater nitrate contamination in the Seine basin. The STICS-MODCOU modelling chain. Science of the Total Environment 2007, 375:33-47.
46. Lin G.-F., Chen L.-H. A spatial interpolation method based on radial basis function networks incorporating a semivariogram model. Journal of Hydrology 2004, 288(3-4):288-298.
47. Luchetta A., Manetti S. A real time hydrological forecasting system using a fuzzy clustering approach. Computers & Geosciences 2003, 29:1111-1117.
48. Lyon S., Seibert J., Lembo A., Walter M., Steenhuis T. Geostatistical investigation into the temporal evolution of spatial structure in a shallow water table. Hydrology and Earth Systems Science 2006, 10(1):113-125.
49. Marcé R., Comerma M., García J., Armengo J. A neuro-fuzzy modeling tool to estimate fluvial nutrient loads in watersheds under time-varying human impact. Limnology and Oceanography: Methods 2004, 2:342-355.
50. Minns A., Hall M. Rainfall-Runoff Modelling 2004, AA Balkema Publishers, pp. 157-176 (Chapter 9).
51. Morshed J., Kaluarachchi J.J. Application of artificial neural network and genetic algorithm in flow and transport simulations. Advances in Water Resources 1998, 22(2):145-158.
52. Nayak P., Sudheer K., Ragan D., Ramasastri K. A neuro-fuzzy computing technique for modeling hydrological time series. Journal of Hydrology 2004, 291:52-66.
53. Pai T., Wan T., Hsu S., Chang T., Tsai Y., Lin C., Hu H., Yu L. Using fuzzy inference system to improve neural network for predicting hospital wastewater treatment plant effluent. Computers and Chemical Engineering 2009, 33:1272-1278.
54. Pardo-Igúzquiza E., Chica-Olmo M. KRIGRADI: A cokriging program for estimating the gradient of spatial variables from sparse data. Computers & Geosciences 2007, 33:497-512.
55. Pardo-Igúzquiza E., Chica-Olmo M., Garcia-Soldado M., Luque-Espinar J.A. Using semivariogram parameter uncertainty in hydrogeological applications. Ground Water 2009, 47(1):25-34.
56. Polus E., Flipo N., de Fouquet C., Poulin M. Geostatistics for assessing the efficiency of distributed physically-based water quality model. Application to nitrates in the Seine river. Hydrological Processes 2011, 25(2):217-233.
57. Pratihar D. Soft Computing 2008, Alpha Science International Ltd.
58. Renard F., Jeannee N. Estimating transmissivity fields and their influence on flow and transport: The case of Champagne mounts. Water Resources Research 2008, 44:1-12.
59. Rivest M., Marcotte D., Pasquier P. Hydraulic head field estimation using kriging with an external drift: A way to consider conceptual model information. Journal of Hydrology 2008, 361(3-4):349-361.
60. Sahoo G., Ray C., Wade H. Pesticide prediction in ground water in North Carolina domestic wells using artificial neural networks. Ecological Modeling 2005, 183:29-46.
61. Samani N., Gohari-Moghadam M., Safavi A. A simple neural network model for the determination of aquifer parameters. Journal of Hydrology 2007, 340:1-11.
62. Sun Y., Kang S., Li F., Zhang L. Comparison of interpolation methods for depth to groundwater and its temporal and spatial variations in the Minqin Oasis of northwest China. Environmental Modelling & Software 2009, 24(10):1163-1170.
63. Takagi T., Sugeno M. Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics 1985, 15(1):116-132.
64. Theodossiou N., Latinopoulos P. Evaluation and optimisation of groundwater observation networks using the kriging methodology. Environmental Modelling & Software 2006, 21(7):991-1000.
65. Tutmez B., Hatipoglu Z., Kaymak U. Modelling electrical conductivity of groundwater using an adaptive neuro-fuzzy inference system. Computers & Geosciences 2006, 32(4):421-433.
66. Wang W.-C., Chau K.-W., Cheng C.-T., Qiu L. A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. Journal of Hydrology 2009, 374:294-306.
67. Yan S., Minsker B. Optimal groundwater remediation design using an adaptive neural network genetic algorithm. Water Resources Research 2006, 42(5):W05407.
68. Yurdusev M.A., Firat M. Adaptive neuro fuzzy inference system approach for municipal water consumption modeling: An application to Izmir, Turkey. Journal of Hydrology 2009, 365:225-234.
69. Zadeh L. Fuzzy sets. Information and Control 1965, 8:338-353.
70. Zanchettin, C., Minku, F., Ludernir, T., 2005. Design of experiments in neuro-fuzzy systems. In: Proceedings of the Fifth International Conference on Hybrid Intelligent Systems (HIS05), IEEE.