Data fusion methods for integrating data-driven hydrological models

Studies in Computational Intelligence

Volumn 79, Issue , 2008, Pages 1-18

  See, Linda ^a  

^a UNIVERSITY OF LEEDS   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 38349078919     PISSN: 1860949X     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-540-75384-1_1     Document Type: Article

References (69)

1. Abbott MB, Bathurst JC, Cunge JA, O'Connell PE, Rasmussen J (1986) An introduction to the European Hydrological System - Système Hydrologique Européen, "SHE", 2: Structure of physically-based, distributed modelling system. Journal of Hydrology 87: 61-77
2. Abrahart RJ, See LM (2002) Multi-model data fusion for river flow forecasting: An evaluation of six alternative methods based on two contrasting catchments. Hydrology and Earth System Sciences 6: 655-670
3. Ajami NK, Duan QY, Gao XG, Sorooshian S (2006) Multimodel combination techniques for analysis of hydrological simulations: Application to distributed model intercomparison project results. J. Hydrometeorol. 7(4): 755-768
4. Anctil F, Lauzon N (2004) Generalisation for neural networks through data sampling and training procedures, with applications to streamflow predictions. Hydrology and Earth System Sciences 8: 940-958
5. Babovic V, Keijzer M (2002) Rainfall runoff modelling based on genetic programming. Nordic Hydrology 33(5): 331-346
6. Breiman L (1996) Bagging predictors. Machine Learning 26: 123-140
7. Brion GM, Neelakantan TR, Lingireddy S (2001) Using neural networks to predict peak Cryptosporidium concentrations. Journal of the American Water Works Association 93: 99-105
8. Box GEP, Jenkins GM (1976) Time Series Analysis: Forecasting and Control. Holden-Day: Oakland
9. Chaves P, Chang F-J (2006) Simulation of multiple water quality parameters using artificial neural networks. In: Proceedings of the 7th International Conference on Hydroinformatics, September 4-8 2006, Nice, pp 749-756
10. Chaves P, Kojiri T, Yamashiki Y (2003) Optimization of storage reservoir considering water quantity and quality. Hydrol. Processes 17: 2769-2793
11. Clair TA, Ehrman JM (1998) Using neural networks to assess the influence of changing seasonal climates in modifying discharge, dissolved organic carbon, and nitrogen export in eastern Canadian rivers. Water Resources Research 34(3): 447-455
12. Dasarathy BV (1997) Sensor fusion potential exploitation - Innovative architectures and illustrative applications. Proceedings of the IEEE 85: 24-38
13. Dawson CW, Abrahart RJ, See LM (2007) HydroTest: A web-based toolbox of statistical measures for the standardised assessment of hydrological forecasts. Environmental Modelling and Software 27: 1034-1052
14. di Pierro F, Khu ST, Savic D (2006) From single-objective to multiple-objective multiplerainfall events automatic calibration of urban storm water runoff models using genetic algorithms. Water Science and Technology 54 (6-7): 57-64
15. Gangopadyay S, Gautam TR, Gupta AD (1999) Subsurface Characterization Using Artificial Neural Network and GIS. J. Comp. in Civ. Engrg. 13: 153-161
16. Georgakakos KP, Seo DJ, Gupta H, Schaake J, Butts MB (2004) Towards the characterization of streamflow simulation uncertainty through multimodel ensembles. J. Hydrol. 298(1-4): 222-241
17. Giustolisi O, Simeone V (2006) Optimal design of artificial neural networks by a multiobjective strategy: Groundwater level predictions. Hydrological Sciences Journal 51(3): 502-523
18. Gumrah F, Oz B, Guler B, Evin S (2000) The application of artificial neural networks for the prediction of water quality of polluted aquifer. Water Air and Soil Pollution 119: 275-294
19. Ha SR, Park SY, Park DH (2003) Estimation of urban runoff and water quality using remote sensing and artificial intelligence. Water Science and Technology 47: 319-325
20. Hall DL (1992) Mathematical techniques in multisensor data fusion. Artech House: Boston
21. Hall DL, Linas J (1997) An introduction to multisensor data fusion. Proceedings of the IEEE 85 (1): 6-23
22. Hall DL, Linas J (2001) Handbook of multisensor data fusion. CRC Press: Boca Raton FL
23. Haykin S (1999) Neural networks: A comprehensive foundation. McMillan: New York
24. Heppenstall AJ, See LM, Abrahart RJ (2006) Advances in Neural Network Hydrological Modelling: An Adaptive Co-evolutionary Approach. In: Proceedings of the 7th International Conference on Hydroinformatics, September 4-8 2006, Nice, pp 966-973
25. Jain A, Srinivasulu S (2004) Development of effective and efficient rainfall-runoff models using integration of deterministic, real-coded genetic algorithms and artificial neural network techniques. Water Resources Research 40, art. no. W04302
26. Jang J-SR, Sun C-T, Mizutani E (1997) Neuro-fuzzy and soft computing. Prentice Hall: NJ
27. Jarvie HP, Neal C, Robson AJ (1997) The geography of the Humber catchment. The Science of the Total Environment 194/195: 87-99
28. Karlsson M, Yakowitz S (1987) Nearest neighbour methods for non-parametric rainfall runoff forecasting. Water Resour. Res. 23: 1300-1308
29. Keijzer M, Babovic V (2000) Genetic programming, ensemble methods and the bias/variance tradeoff - Introductory investigations. Lecture Notes in Computer Science 1802: 76-90
30. Khan SA, See LM (2006) Rainfall-runoff modelling using data driven and statistical methods, In: Proceedings of ICAST 2006, Islamabad, Pakistan, 2-3 September 2006, IEEE Catalog number: 06X1482, ISBN for CD-Rom: 1-4244-0515-7, ISBN for printed version: 1-4244-0514-9
31. Khondker MUH, Wilson G, Klinting A (1998) Application of neural networks in real time flash flood forecasting. In: Babovic V, Larsen CL (eds) Proceedings of the Third International Conference on HydroInformatics. AA Balkema: Rotterdam, pp 777-782
32. Kitanidis PK, Bras RL (1980) Real-time forecasting with a conceptual hydrologic model. 2. Application and results. Water Resour. Res. 16(6): 1034-1044
33. Kohonen T (1984) Self-organization and associative memory. Springer-Verlag: Berlin
34. Kojiri T (1999) Cluster-based hydrological analysis and prediction of long-term climate change and drought discharge. Hydrological Extremes: Understanding, Prediction Mitigating, IAHS Publ. No.255, 45-54
35. Kojiri T, Hanatani S (2002) Knowledge-based decision support system for reservoir operation with knowledge acquisition process on flood prediction and risk accuracy. Flood Defence (2002, Vol.II, Beijing, 1430-1437
36. Kosko B (1992) Neural networks and fuzzy systems: A dynamical systems approach to machine intelligence. Prentice Hall Inc.: NJ
37. Kuchment LS, Demidov VN, Naden PS, Cooper DM, Broadhurst P (1996) Rainfall-runoff modelling of the Ouse basin, North Yorkshire: An application of a physically-based distributed model. Journal of Hydrology 181: 323-342
38. Lane S (2002) More floods, less rain: Changing hydrology in a Yorkshire context. The Yorkshire and Humber Regional Review 11: 18-19
39. Law M, Wass P, Grimshaw D (1997) The hydrology of the Humber catchment. The Science of the Total Environment 194/195: 119-128
40. Lindsay JB, Shang JQ, Rowe RK (2002) Using complex permittivity and artificial neural networks for contaminant prediction. J. Envir. Engrg. 128: 740-747
41. Longfield S (1998) River response to recent environmental change in the Yorkshire Ouse basin, northern England. Ph.D. thesis, University of Leeds
42. Maier HR, Dandy G (1996) The use of artificial neural networks for the prediction of water quality parameters. Water Resources Research 32: 1013-1022
43. Marshall L, Sharma A, Nott D (2006) Modeling the catchment via mixtures: Issues of model specification and validation. Water Resour. Res. 42(11): W07410
44. Minns AW, Hall MJ (1996) Artificial neural networks as rainfall-runoff models. Hydrological Sciences Journal 41: 399-417
45. Nayak PC, Sudheer KP, Rangan DM, Ramasastri KS (2004) A neuro-fuzzy computing technique for modeling hydrological time series. Journal of Hydrology 291: 52-66
46. Oudin L, Andreassian V, Mathevet T, Perrin C, Michel C (2006) Dynamic averaging of rainfall-runoff model simulations from complementary model parameterizations. Water Resour. Res. 42(7): W07410
47. Quinlan JR (1992) Learning with continuous classes. In: Proceedings of AI'92, 5th Australian Joint Conference on Artificial Intelligence, World Scientific, Singapore, pp 343-348
48. Salas JD, Markus M, Tokar AS (2000) Streamflow forecasting based on artificial neural networks. In: Govindaraju RS, Ramachandra Rao A (eds) Artificial Neural Networks in Hydrology, Kluwer Academic Publishers, Dordrecht, pp 23-51
49. Schmitz GH, Schutze N, Petersohn U (2002) New strategy for optimizing water application under trickle irrigation. J. Irrig. and Drain. Engrg. 128: 287-297
50. See LM, Openshaw S (1999) Applying soft computing approaches to river level forecasting. Hydrological Sciences Journal 44: 763-778
51. See LM, Openshaw S (2000) A hybrid multi-model approach to river level forecasting. Hydrological Sciences Journal 45: 523-536
52. See LM, Shamseldin AY, Abrahart RJ, Dawson CW, Heppenstall AJ (2006) Rainfall-runoff modelling using a committee approach, EGU, Vienna, Austria, 5-9 Apr 2006
53. Shamseldin AY (1997) Application of a neural network to rainfall-runoff modelling. Journal of Hydrology 199: 272-294
54. Shamseldin AY, O'Connor KM (1996) A nearest neighbour linear perturbation model for river flow forecasting. J. of Hydrology 179: 353-375
55. Shamseldin AY, Nasr AE, O'Connor KM (2002) Comparison of different forms of the multilayer feed-forward neural network method used for river flow forecasting. Hydrology and Earth System Sciences 6: 671-684
56. Singh RM, Datta B, Jain A (2003) Identification of unknown groundwater pollution sources using artificial neural networks. J. Water Resour. Plng. and Mgmt 130: 506-514
57. Smith J, Eli RN (1995) Neural network models of rainfall-runoff process. Journal of Water Resources Planning and Management 121: 499-509
58. Smith D, Singh S (2006) Approaches to multisensor data fusion in target tracking: A survey. IEEE Transactions on Knowledge and Data Engineering 18: 1696-1710
59. Solomatine DP (2005) Data-driven modelling and computational intelligence methods in hydrology. Encyclopedia of Hydrological Sciences, Wiley
60. Solomatine DP, Dulal KN (2003) Model tree as an alternative to neural network in rainfall-runoff modelling. Hydrological Sciences Journal 48: 399-411
61. Solomatine DP, Siek MB (2006) Modular learning models in forecasting natural phenomena. Neural Networks 19: 215-224
62. Toth E, Brath A, Montanari A (2000) Comparison of short-term rainfall prediction models for real-time flood forecasting. J. of Hydrology 239: 132-147
63. US Department of Defense (1991) Data fusion subpanel of the joint directors of Laboratories, Technical Panel for C3, Data Fusion Lexicon
64. Wheater HS, Jakeman AJ, Beven KJ (1993) Progress and directions in rainfall-runoff modelling. In: Modelling Change in Environmental Systems, Jakeman AJ, Beck MB, McAleer MJ (eds), 101-132, Wiley
65. Xiong LH, Shamseldin AY, O'Connor KM (2001) A non-linear combination of the forecasts of rainfall-runoff models by the first-order Takagi-Sugeno fuzzy system. J. of Hydrology 245: 196-217
66. Yan SQ, Minsker B (2006) Optimizing groundwater remediation designs under uncertainty using dynamic surrogates and noisy genetic algorithms. In: Proceedings of the 7th International Conference on HydroInformatics, September 4-8 2006, Nice, pp 2513-2520
67. Yang C-C, Prasher SO, Lacroix R, Sreekanth S, Patni NK, Masse L (1997) Artificial neural network model for subsurface-drained farmlands. J. Irrig. and Drain. Engrg., ASCE 123: 285-292
68. Young P (2002) Advances in real-time flood forecasting. Philosophical Transactions of the Royal Society London A 360(1796): 1433-1450, DOI: 1410.1098/rsta.2002.1008
69. Young P (2003) Top-down and data-based mechanistic modelling of rainfall-flow dynamics at the catchment scale. Hydrol. Processes 17(11): 2195-2217