-
1
-
-
0036698601
-
Multi-model data fusion for river flow forecasting: an evaluation of six alternative methods based on two contrasting catchments
-
Abrahart R.J., See L. Multi-model data fusion for river flow forecasting: an evaluation of six alternative methods based on two contrasting catchments. Hydrol. Earth Syst. Sci. 2002, 6(4):655-670.
-
(2002)
Hydrol. Earth Syst. Sci.
, vol.6
, Issue.4
, pp. 655-670
-
-
Abrahart, R.J.1
See, L.2
-
2
-
-
34648849221
-
Neural network modelling of non-linear hydrological relationship
-
Abrahart R.J., See L. Neural network modelling of non-linear hydrological relationship. Hydrol. Earth Syst. Sci. 2007, 11(4):1563-1597.
-
(2007)
Hydrol. Earth Syst. Sci.
, vol.11
, Issue.4
, pp. 1563-1597
-
-
Abrahart, R.J.1
See, L.2
-
3
-
-
33344463490
-
Water level forecasting through fuzzy logic and neural network approaches
-
Alvisi S., Mascellani G., Franchini M., Bardossy A. Water level forecasting through fuzzy logic and neural network approaches. Hydrol. Earth Syst. Sci. 2006, 10(1):1-17.
-
(2006)
Hydrol. Earth Syst. Sci.
, vol.10
, Issue.1
, pp. 1-17
-
-
Alvisi, S.1
Mascellani, G.2
Franchini, M.3
Bardossy, A.4
-
4
-
-
0034174280
-
Artificial neural networks in hydrology. I: Preliminary concepts
-
ASCE Task Committee on the application of ANN in Hydrology
-
ASCE Task Committee on the application of ANN in Hydrology Artificial neural networks in hydrology. I: Preliminary concepts. J. Hydrol. Eng. 2000, 5(2):115-123.
-
(2000)
J. Hydrol. Eng.
, vol.5
, Issue.2
, pp. 115-123
-
-
-
5
-
-
0034174396
-
Artificial neural networks in hydrology. II: hydrological applications
-
ASCE Task Committee on the Application of ANN in Hydrology
-
ASCE Task Committee on the Application of ANN in Hydrology Artificial neural networks in hydrology. II: hydrological applications. J. Hydrol. Eng. 2000, 5(2):124-137.
-
(2000)
J. Hydrol. Eng.
, vol.5
, Issue.2
, pp. 124-137
-
-
-
8
-
-
0027009437
-
The future of distributed models: model calibration and uncertainty prediction
-
Beven K.J., Binley A.M. The future of distributed models: model calibration and uncertainty prediction. Hydrol. Proc. 1992, 6:279-298.
-
(1992)
Hydrol. Proc.
, vol.6
, pp. 279-298
-
-
Beven, K.J.1
Binley, A.M.2
-
9
-
-
33644526990
-
A manifesto for the equifinality thesis
-
Beven K.J. A manifesto for the equifinality thesis. J. Hydrol. 2006, 320:18-36.
-
(2006)
J. Hydrol.
, vol.320
, pp. 18-36
-
-
Beven, K.J.1
-
10
-
-
68949135430
-
Tools for the assessment of hydrological ensemble forecasts obtained by neural networks
-
Boucher M.A., Perreault L., Anctil F. Tools for the assessment of hydrological ensemble forecasts obtained by neural networks. J. Hydroinf. 2009, 11(3):297-306.
-
(2009)
J. Hydroinf.
, vol.11
, Issue.3
, pp. 297-306
-
-
Boucher, M.A.1
Perreault, L.2
Anctil, F.3
-
11
-
-
0032688155
-
River flood forecasting with neural network model
-
Campolo M., Andreussi P., Soldati A. River flood forecasting with neural network model. Water Resour. Res. 1999, 35(4):1191-1197.
-
(1999)
Water Resour. Res.
, vol.35
, Issue.4
, pp. 1191-1197
-
-
Campolo, M.1
Andreussi, P.2
Soldati, A.3
-
12
-
-
0038240745
-
Artificial neural network approach to flood forecasting in the river Arno
-
Campolo M., Andreussi P., Soldati A. Artificial neural network approach to flood forecasting in the river Arno. Hydrol. Sci. J. 2003, 48(3):381-398.
-
(2003)
Hydrol. Sci. J.
, vol.48
, Issue.3
, pp. 381-398
-
-
Campolo, M.1
Andreussi, P.2
Soldati, A.3
-
13
-
-
0035340711
-
A counterpropagation fuzzy-neural network modelling approach to real time streamflow prediction
-
Chang F.J., Chen Y.C. A counterpropagation fuzzy-neural network modelling approach to real time streamflow prediction. J. Hydrol. 2001, 245:153-164.
-
(2001)
J. Hydrol.
, vol.245
, pp. 153-164
-
-
Chang, F.J.1
Chen, Y.C.2
-
14
-
-
15444365276
-
Fuzzy exemplar-based inference system for flood forecasting
-
Chang L.C., Chang F.J., Tsai Y.H. Fuzzy exemplar-based inference system for flood forecasting. Water Resour. Res. 2005, 41(W02005). 10.1029/2004WR003037.
-
(2005)
Water Resour. Res.
, vol.41
, Issue.W02005
-
-
Chang, L.C.1
Chang, F.J.2
Tsai, Y.H.3
-
16
-
-
0024192140
-
Effective and efficient global optimization for conceptual rainfall runoff models
-
Duan Q., Sorooshian S., Gupta V.K. Effective and efficient global optimization for conceptual rainfall runoff models. Water Resour. Res. 1992, 24(7):1163-1173.
-
(1992)
Water Resour. Res.
, vol.24
, Issue.7
, pp. 1163-1173
-
-
Duan, Q.1
Sorooshian, S.2
Gupta, V.K.3
-
18
-
-
0026015424
-
Comparative analysis of several conceptual rainfall-runoff models
-
Franchini M., Pacciani M. Comparative analysis of several conceptual rainfall-runoff models. J. Hydrol. 1991, 122:161-219.
-
(1991)
J. Hydrol.
, vol.122
, pp. 161-219
-
-
Franchini, M.1
Pacciani, M.2
-
19
-
-
0021518209
-
Gibbs distributions and the Bayesian restoration of images
-
Geman S., Geman D. Gibbs distributions and the Bayesian restoration of images. IEEE Trans. Pattern Anal. Mach. Intell. 1984, 6(2):721-741.
-
(1984)
IEEE Trans. Pattern Anal. Mach. Intell.
, vol.6
, Issue.2
, pp. 721-741
-
-
Geman, S.1
Geman, D.2
-
20
-
-
84967485903
-
How does the brain builds a cognitive code?
-
Grossberg S. How does the brain builds a cognitive code?. Psychol. Rev. 1980, 88:375-407.
-
(1980)
Psychol. Rev.
, vol.88
, pp. 375-407
-
-
Grossberg, S.1
-
21
-
-
0028543366
-
Training feedforward networks with the Marquardt algorithm
-
Hagan M.T., Menhaj M. Training feedforward networks with the Marquardt algorithm. IEEE Tran. Neural Net. 1994, 5(6):989-993.
-
(1994)
IEEE Tran. Neural Net.
, vol.5
, Issue.6
, pp. 989-993
-
-
Hagan, M.T.1
Menhaj, M.2
-
23
-
-
0020118274
-
Neural networks and physical systems with emergent collective computational abilities
-
Hopfield J.J. Neural networks and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. U S A 1982, 79:2554-2558.
-
(1982)
Proc. Natl. Acad. Sci. U S A
, vol.79
, pp. 2554-2558
-
-
Hopfield, J.J.1
-
24
-
-
0024880831
-
Multilayer feedforward networks are universal approximators
-
Hornik K., Stinchombe M., White H. Multilayer feedforward networks are universal approximators. Neural. Netw. 1989, 2:359-366.
-
(1989)
Neural. Netw.
, vol.2
, pp. 359-366
-
-
Hornik, K.1
Stinchombe, M.2
White, H.3
-
25
-
-
0029413797
-
Artificial neural network modeling of the rainfall-runoff process
-
Hsu K.L., Gupta H.V., Sorooshian S. Artificial neural network modeling of the rainfall-runoff process. Water Resour. Res. 1995, 31(10):2517-2530.
-
(1995)
Water Resour. Res.
, vol.31
, Issue.10
, pp. 2517-2530
-
-
Hsu, K.L.1
Gupta, H.V.2
Sorooshian, S.3
-
26
-
-
17044442585
-
Development of a fuzzy logic-based rainfall-runoff model
-
Hundecha Y., Bardossy A., Theisen H.W. Development of a fuzzy logic-based rainfall-runoff model. Hydrol. Sci. J. 2001, 46(3):363-376.
-
(2001)
Hydrol. Sci. J.
, vol.46
, Issue.3
, pp. 363-376
-
-
Hundecha, Y.1
Bardossy, A.2
Theisen, H.W.3
-
27
-
-
84943257282
-
Fuzzy neural networks with fuzzy weights and fuzzy biases
-
Ishibuchi H., Okada H., Tanaka H. Fuzzy neural networks with fuzzy weights and fuzzy biases. Proc. ICNN'93 1993, 1650-1655.
-
(1993)
Proc. ICNN'93
, pp. 1650-1655
-
-
Ishibuchi, H.1
Okada, H.2
Tanaka, H.3
-
28
-
-
0028739351
-
A fuzzy neural network with trapezoid fuzzy weights
-
Ishibuchi H., Morioka K., Tanaka H. A fuzzy neural network with trapezoid fuzzy weights. IEEE, New York 1994, 228-233.
-
(1994)
IEEE, New York
, pp. 228-233
-
-
Ishibuchi, H.1
Morioka, K.2
Tanaka, H.3
-
29
-
-
0035313816
-
Fuzzy regression using asymmetric fuzzy coefficients and fuzzified neural networks
-
Ishibuchi H., Nii M. Fuzzy regression using asymmetric fuzzy coefficients and fuzzified neural networks. Fuzzy Set. System. 2001, 119:273-290.
-
(2001)
Fuzzy Set. System.
, vol.119
, pp. 273-290
-
-
Ishibuchi, H.1
Nii, M.2
-
30
-
-
34249082149
-
Development of a possibilistic method for the evaluation of predictive uncertainty in rainfall-runoff modelling
-
Jacquin A.P., Shamseldin A.Y. Development of a possibilistic method for the evaluation of predictive uncertainty in rainfall-runoff modelling. Water Resour. Res. 2007, 43(W04425). 10.1029/2006WR005072.
-
(2007)
Water Resour. Res.
, vol.43
, Issue.W04425
-
-
Jacquin, A.P.1
Shamseldin, A.Y.2
-
31
-
-
0027601884
-
ANFIS: adaptive network based fuzzy inference systems
-
Jang J. ANFIS: adaptive network based fuzzy inference systems. IEEE. Trans. Syst. Man. Cybern. 1993, 23(3):665-683.
-
(1993)
IEEE. Trans. Syst. Man. Cybern.
, vol.23
, Issue.3
, pp. 665-683
-
-
Jang, J.1
-
33
-
-
33748029144
-
Bayesian neural network for rainfall-runoff modelling
-
Khan M.S., Coulibaly P. Bayesian neural network for rainfall-runoff modelling. Water Resour. Res. 2006, 42(W07409). 10.1029/2005WR003971.
-
(2006)
Water Resour. Res.
, vol.42
, Issue.W07409
-
-
Khan, M.S.1
Coulibaly, P.2
-
34
-
-
31444455186
-
Bayesian training of artificial neural networks used for water resources modelling
-
Kingston G.B., Lambert M.F., Maier H.R. Bayesian training of artificial neural networks used for water resources modelling. Water Resour. Res. 2005, 41(W12409). 10.1029/2005WR004152.
-
(2005)
Water Resour. Res.
, vol.41
, Issue.W12409
-
-
Kingston, G.B.1
Lambert, M.F.2
Maier, H.R.3
-
35
-
-
0015331348
-
Correlation matrix memories
-
Kohonen T. Correlation matrix memories. IEEE. Trans. Comput. 1972, 21:353-359.
-
(1972)
IEEE. Trans. Comput.
, vol.21
, pp. 353-359
-
-
Kohonen, T.1
-
36
-
-
0032853041
-
Bayesian theory of probabilistic forecasting via deterministic hydrologic model
-
Krzysztofowicz R. Bayesian theory of probabilistic forecasting via deterministic hydrologic model. Water Resour. Res. 1999, 35(9):2739-2750.
-
(1999)
Water Resour. Res.
, vol.35
, Issue.9
, pp. 2739-2750
-
-
Krzysztofowicz, R.1
-
37
-
-
0035426007
-
The case for probabilistic forecasting in hydrology
-
Krzysztofowicz R. The case for probabilistic forecasting in hydrology. J. Hydrol. 2001, 249:2-9. 10.1016/S0022-1694(01)00420-6.
-
(2001)
J. Hydrol.
, vol.249
, pp. 2-9
-
-
Krzysztofowicz, R.1
-
38
-
-
0035312886
-
Bayesian approach for neural networks-review and case studies
-
Lampinen J., Vehtari A. Bayesian approach for neural networks-review and case studies. Neural. Netw. 2001, 14(3):257-274. 10.1016/S0893-6080(00)00098-8.
-
(2001)
Neural. Netw.
, vol.14
, Issue.3
, pp. 257-274
-
-
Lampinen, J.1
Vehtari, A.2
-
39
-
-
0033957764
-
Neural networks for prediction and forecasting of water resources variables: a review of modelling issue and applications
-
Maier H.R., Dandy G.C. Neural networks for prediction and forecasting of water resources variables: a review of modelling issue and applications. Environ. Model. Software 2000, 15:101-124.
-
(2000)
Environ. Model. Software
, vol.15
, pp. 101-124
-
-
Maier, H.R.1
Dandy, G.C.2
-
40
-
-
33748808177
-
Hydrological forecasting uncertainty assessment: incoherence of the GLUE methodology
-
Mantovan P., Todini E. Hydrological forecasting uncertainty assessment: incoherence of the GLUE methodology. J. Hydrol. 2006, 330:368-381. 10.1016/j.jhydrol.2006.04.046.
-
(2006)
J. Hydrol.
, vol.330
, pp. 368-381
-
-
Mantovan, P.1
Todini, E.2
-
41
-
-
0028754808
-
Fuzzy regression analysis by fuzzy neural networks and its application
-
in: Proceedings of the Third IEEE Conference on Fuzzy Systems
-
Miyazaki, A., Known, K., Ishibuchi, H. and Tanaka, H., 1994. Fuzzy regression analysis by fuzzy neural networks and its application. in: Proceedings of the Third IEEE Conference on Fuzzy Systems, vol. 1, pp. 52-57.
-
(1994)
, vol.1
, pp. 52-57
-
-
Miyazaki, A.1
Known, K.2
Ishibuchi, H.3
Tanaka, H.4
-
42
-
-
33947533125
-
What do we mean by 'uncertainty'? The need for a consistent wording about uncertainty assessment in hydrology
-
Montanari A. What do we mean by 'uncertainty'? The need for a consistent wording about uncertainty assessment in hydrology. Hydrol. Proc. 2007, 21:841-845. 10.1002/hyp.6623.
-
(2007)
Hydrol. Proc.
, vol.21
, pp. 841-845
-
-
Montanari, A.1
-
43
-
-
72149128601
-
Estimating the uncertainty of hydrological forecasts: a statistical approach
-
Montanari A., Grossi G. Estimating the uncertainty of hydrological forecasts: a statistical approach. Water Resour. Res. 2008, 44(W00B08). 10.1029/2008WR006897.
-
(2008)
Water Resour. Res.
, vol.44
, Issue.W00B08
-
-
Montanari, A.1
Grossi, G.2
-
44
-
-
1942490118
-
A neuro-fuzzy computing technique for modelling hydrological time series
-
Nayak P.C., Sudheer K.P., Rangan D.M., Ramasastri K.S. A neuro-fuzzy computing technique for modelling hydrological time series. J. Hydrol. 2004, 291:52-66.
-
(2004)
J. Hydrol.
, vol.291
, pp. 52-66
-
-
Nayak, P.C.1
Sudheer, K.P.2
Rangan, D.M.3
Ramasastri, K.S.4
-
45
-
-
78650527502
-
-
Bayesian training of backpropagation networks by the hybrid Monte Carlo method. Tech. Rep. CRG-TR-92-1, Dep. of Comput. Sci., Univ. of Toronto, Toronto, Ont., Canada.
-
Neal, R.M. 1992. Bayesian training of backpropagation networks by the hybrid Monte Carlo method. Tech. Rep. CRG-TR-92-1, Dep. of Comput. Sci., Univ. of Toronto, Toronto, Ont., Canada.
-
(1992)
-
-
Neal, R.M.1
-
46
-
-
16344393109
-
Constructing rule-bases for multivariable fuzzy control by self-learning - part I: system structure and learning algorithms
-
Nie J. Constructing rule-bases for multivariable fuzzy control by self-learning - part I: system structure and learning algorithms. Int. J. Syst. Sci. 1993, 24:111-127.
-
(1993)
Int. J. Syst. Sci.
, vol.24
, pp. 111-127
-
-
Nie, J.1
-
47
-
-
0035889604
-
Fuzzy conceptual rainfall-runoff models
-
Özelkan E.C., Duckstein L. Fuzzy conceptual rainfall-runoff models. J. Hydrol. 2001, 253:41-68.
-
(2001)
J. Hydrol.
, vol.253
, pp. 41-68
-
-
Özelkan, E.C.1
Duckstein, L.2
-
48
-
-
0008339101
-
-
Springer Verlag, Germany, A. Bechem, M. Grostschel, B. Korte (Eds.)
-
Powell M.J.D. Variable Metric Methods for Constrained Optimization, Mathematical Programming: The State of the Art 1983, 288-311. Springer Verlag, Germany. A. Bechem, M. Grostschel, B. Korte (Eds.).
-
(1983)
Variable Metric Methods for Constrained Optimization, Mathematical Programming: The State of the Art
, pp. 288-311
-
-
Powell, M.J.D.1
-
49
-
-
0036171051
-
Fuzzy approach for Analysis of pipe networks
-
Revelli R., Ridolfi L. Fuzzy approach for Analysis of pipe networks. J. Hydraul. Eng. 2002, 128(1):93-101.
-
(2002)
J. Hydraul. Eng.
, vol.128
, Issue.1
, pp. 93-101
-
-
Revelli, R.1
Ridolfi, L.2
-
50
-
-
11144273669
-
The perceptron: a probabilistic model for information storage and organization in the brain
-
Rosenblatt F. The perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev. 1958, 65:386-408.
-
(1958)
Psychol. Rev.
, vol.65
, pp. 386-408
-
-
Rosenblatt, F.1
-
51
-
-
51649147836
-
NLQPL: a Fortran-subroutine solving constrained nonlinear programming problems
-
Schitlowski K. NLQPL: a Fortran-subroutine solving constrained nonlinear programming problems. Oper. Res. 1985, 5:485-500.
-
(1985)
Oper. Res.
, vol.5
, pp. 485-500
-
-
Schitlowski, K.1
-
52
-
-
0034254025
-
A hybrid multi-model approach to river level forecasting
-
See L., Openshaw S. A hybrid multi-model approach to river level forecasting. Hydrol. Sci. J. 2000, 45(4):523-536.
-
(2000)
Hydrol. Sci. J.
, vol.45
, Issue.4
, pp. 523-536
-
-
See, L.1
Openshaw, S.2
-
53
-
-
34249844193
-
Hydroinformatics: computational intelligence and technological developments in water science applications
-
See L., Solomatine D.P., Abrahart R., Toth E. Hydroinformatics: computational intelligence and technological developments in water science applications. Hydrol. Sci. J. 2007, 52(3):391-396.
-
(2007)
Hydrol. Sci. J.
, vol.52
, Issue.3
, pp. 391-396
-
-
See, L.1
Solomatine, D.P.2
Abrahart, R.3
Toth, E.4
-
54
-
-
33645987256
-
Machine learning approaches for estimation of prediction interval for the model output
-
Shrestha D.L., Solomatine D.P. Machine learning approaches for estimation of prediction interval for the model output. Neural. Netw. 2006, 19(2):225-235.
-
(2006)
Neural. Netw.
, vol.19
, Issue.2
, pp. 225-235
-
-
Shrestha, D.L.1
Solomatine, D.P.2
-
55
-
-
0037565156
-
Model trees as an alternative to neural networks in rainfall runoff modelling
-
Solomatine D.P., Dulal K.N. Model trees as an alternative to neural networks in rainfall runoff modelling. Hydrol. Sci. J. 2003, 48(3):399-411.
-
(2003)
Hydrol. Sci. J.
, vol.48
, Issue.3
, pp. 399-411
-
-
Solomatine, D.P.1
Dulal, K.N.2
-
56
-
-
34249775216
-
Development of a low flow forecasting model using the M5 machine learning method
-
Stravs L., Brilly M. Development of a low flow forecasting model using the M5 machine learning method. Hydrol. Sci. J. 2007, 52(3):466-477.
-
(2007)
Hydrol. Sci. J.
, vol.52
, Issue.3
, pp. 466-477
-
-
Stravs, L.1
Brilly, M.2
-
57
-
-
0034174356
-
Hydrological forecasting using neural networks
-
Thirumalaiah K., Deo M.C. Hydrological forecasting using neural networks. J. Hydrol. Eng. 2000, 5(2):180-189.
-
(2000)
J. Hydrol. Eng.
, vol.5
, Issue.2
, pp. 180-189
-
-
Thirumalaiah, K.1
Deo, M.C.2
-
58
-
-
37549066943
-
Multistep ahead streamflow forecasting: role of calibration data in conceptual and neural network modeling
-
Toth E., Brath A. Multistep ahead streamflow forecasting: role of calibration data in conceptual and neural network modeling. Water Resour. Res. 2007, 43(W11405). 10.1029/2006WR005383.
-
(2007)
Water Resour. Res.
, vol.43
, Issue.W11405
-
-
Toth, E.1
Brath, A.2
-
59
-
-
78650533379
-
-
MCMC methods for MLP-network and Gaussian process and stuff-a documentation for Matlab toolbox MCMCstuff. Available from
-
Vanhatalo A., Vehtari, A., 2006. MCMC methods for MLP-network and Gaussian process and stuff-a documentation for Matlab toolbox MCMCstuff. Available from: http://www.lce.hut.fi/research/mm/mcmcstuff.
-
(2006)
-
-
Vanhatalo, A.1
Vehtari, A.2
-
60
-
-
70349545766
-
Indices for assessing the prediction bounds of hydrological models and application by generalised likelihood uncertainty estimation
-
Xiong L., Wan M., Wei X., O'Connor K. Indices for assessing the prediction bounds of hydrological models and application by generalised likelihood uncertainty estimation. Hydrol. Sci. J. 2009, 54(5):852-871.
-
(2009)
Hydrol. Sci. J.
, vol.54
, Issue.5
, pp. 852-871
-
-
Xiong, L.1
Wan, M.2
Wei, X.3
O'Connor, K.4
-
61
-
-
34248666540
-
Fuzzy sets
-
Zadeh L.A. Fuzzy sets. Inform. Control 1965, 8:338-353.
-
(1965)
Inform. Control
, vol.8
, pp. 338-353
-
-
Zadeh, L.A.1
-
62
-
-
62949213977
-
Estimating uncertainty of streamflow simulation using Bayesian neural networks
-
Zhang X., Liang F., Srinivasan R., Van Liew M. Estimating uncertainty of streamflow simulation using Bayesian neural networks. Water Resour. Res. 2009, 45(W02403). 10.1029/2008WR007030.
-
(2009)
Water Resour. Res.
, vol.45
, Issue.W02403
-
-
Zhang, X.1
Liang, F.2
Srinivasan, R.3
Van Liew, M.4
|