-
1
-
-
0034254196
-
Comparing neural network and autoregressive moving average techniques for the provision of continuous river flow forecasts in two contrasting catchments
-
Abrahart, R. J. and See, L.: Comparing neural network and autoregressive moving average techniques for the provision of continuous river flow forecasts in two contrasting catchments, Hydrol. Process., 14(11), 2157-2172, 2000.
-
(2000)
Hydrol. Process
, vol.14
, Issue.11
, pp. 2157-2172
-
-
Abrahart, R.J.1
See, L.2
-
2
-
-
0036698601
-
Multi-model data fusion for river flow forecasting: An evaluation of six alternative methods based on two contrasting catchments
-
Abrahart, R. J. and 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., 6, 655-670, 2002, http://www.hydrol-earth- syst-sci.net/6/655/2002/.
-
(2002)
Hydrol. Earth Syst. Sci
, vol.6
, pp. 655-670
-
-
Abrahart, R.J.1
See, L.2
-
3
-
-
1442291113
-
Impact of the length of observed records on the performance of ANN and of conceptual parsimonious rainfall-runoff forecasting models
-
Anctil, F., Perrin, Ch., and Andreassian V.: Impact of the length of observed records on the performance of ANN and of conceptual parsimonious rainfall-runoff forecasting models, Environ. Modell. Softw., 19, 357-368, 2004.
-
(2004)
Environ. Modell. Softw.
, vol.19
, pp. 357-368
-
-
Anctil, F.1
Perrin, Ch.2
Andreassian, V.3
-
4
-
-
0034174280
-
ASCE task committee on application of artificial neural networks in hydrology: Artificial neural networks in hydrology. I: Preliminary concepts
-
ASCE Task Committee on Application of Artificial Neural Networks in Hydrology: Artificial neural networks in hydrology. I: preliminary concepts, J. Hydrol. Eng., 5, 115-123, 2000a.
-
(2000)
J. Hydrol. Eng
, vol.5
, pp. 115-123
-
-
-
5
-
-
0034174396
-
ASCE task committee on application of artificial neural networks in hydrology: Artificial neural networks in hydrology II: Hydrologic applications
-
ASCE Task Committee on Application of Artificial Neural Networks in Hydrology: Artificial neural networks in hydrology II: hydrologic applications, J. Hydrol. Eng. 5, 124-137. 2000b.
-
(2000)
J. Hydrol. Eng.
, vol.5
, pp. 124-137
-
-
-
6
-
-
35348971404
-
Comparison of self-organizing maps classification approach with cluster and principal components analysis for large environmental data sets
-
Astel, A., Tsakovski, S., Barbieri, P., and Simeonov, V.: Comparison of self-organizing maps classification approach with cluster and principal components analysis for large environmental data sets, Water Res., 41(19), 4566-4578, 2007.
-
(2007)
Water Res
, vol.41
, Issue.19
, pp. 4566-4578
-
-
Astel, A.1
Tsakovski, S.2
Barbieri, P.3
Simeonov, V.4
-
7
-
-
10644295753
-
Input determination for neural network models in water resources applications. Part 1-background and methodology
-
Bowden, G. J., Dandy G. C., and Maier, H. R.: Input determination for neural network models in water resources applications. Part 1-background and methodology, J. Hydrol., 301, 75-92, 2005
-
(2005)
J. Hydrol
, vol.301
, pp. 75-92
-
-
Bowden, G.J.1
Dandy, G.C.2
Maier, H.R.3
-
8
-
-
0037087735
-
An evaluation of a traditional and a neural net modelling approach to flood forecasting for an upland catchment
-
Cameron, D., Kneale, P., and See, L.: An evaluation of a traditional and a neural net modelling approach to flood forecasting for an upland catchment, Hydrol. Process., 16, 1033-1046, 2002.
-
(2002)
Hydrol. Process
, vol.16
, pp. 1033-1046
-
-
Cameron, D.1
Kneale, P.2
See, L.3
-
9
-
-
0032688155
-
River flood forecasting with neural network model
-
Campolo, M., Andreussi, P., and Soldati, A.: River flood forecasting with neural network model,Water Resour. Res., 35(4), 1191-1197, 1999.
-
(1999)
Water Resour. Res
, vol.35
, Issue.4
, pp. 1191-1197
-
-
Campolo, M.1
Andreussi, P.2
Soldati, A.3
-
10
-
-
34249775808
-
Baseflow separation techniques for modular artificial neural networks modelling in flow forecasting
-
Corzo, G. A. and Solomatine, D. P.: Baseflow separation techniques for modular artificial neural networks modelling in flow forecasting, Hydrolog. Sci. J., 52(3), 491-507, 2007.
-
(2007)
Hydrolog. Sci. J.
, vol.52
, Issue.3
, pp. 491-507
-
-
Corzo, G.A.1
Solomatine, D.P.2
-
11
-
-
0035876630
-
Improving extreme hydrologic events forecasting using a new criterion for artificial neural network selection
-
Coulibaly, P., Bob́ee, B., and Anctil, F.: Improving extreme hydrologic events forecasting using a new criterion for artificial neural network selection, Hydrol. Process., 15, 1533-1536, 2001.
-
(2001)
Hydrol. Process
, vol.15
, pp. 1533-1536
-
-
Coulibaly, P.1
Bob́ee, B.2
Anctil, F.3
-
12
-
-
0032005702
-
An artificial neural network approach to rainfall-runoff modeling
-
Dawson, C. W. and Wilby, R.: An artificial neural network approach to rainfall-runoff modeling, Hydrolog. Sci. J., 43(1), 47-65, 1998.
-
(1998)
Hydrolog. Sci. J.
, vol.43
, Issue.1
, pp. 47-65
-
-
Dawson, C.W.1
Wilby, R.2
-
13
-
-
34249810384
-
Multi-objective performance comparison of an artificial neural network and a conceptual rainfall-runoff model
-
DOI 10.1623/hysj.52.3.397
-
de Vos, N. J. and Rientjes, T. H. M.: Multi-objective performance comparison of an artificial neural network and a conceptual rainfall-runoff model, Hydrolog. Sci. J., 52(3), 397-413, doi:10.1623/hysj.52.3.397, 2007. (Pubitemid 46851530)
-
(2007)
Hydrological Sciences Journal
, vol.52
, Issue.3
, pp. 397-413
-
-
De Vos, N.J.1
Rientjes, T.H.M.2
-
14
-
-
53849113979
-
Multiobjective training of artificial neural networks for rainfall-runoff modeling
-
doi:10.1029/2007WR006734
-
de Vos, N. J. and Rientjes, T. H. M.: Multiobjective training of artificial neural networks for rainfall-runoff modeling, Water Resour. Res., 44, W08434, doi:10.1029/2007WR006734, 2008.
-
(2008)
Water Resour. Res.
, vol.44
-
-
De Vos, N.J.1
Rientjes, T.H.M.2
-
15
-
-
61749084755
-
Selection of input variables for data driven models: An average shifted histogram partial mutual information estimator approach
-
Fernando, T. M. K. G., Maier, H. R., and Dandy, G. C.: Selection of input variables for data driven models: An average shifted histogram partial mutual information estimator approach, J. Hydrol., 367, 165-176, 2009.
-
(2009)
J. Hydrol
, vol.367
, pp. 165-176
-
-
Fernando, T.M.K.G.1
Maier, H.R.2
Dandy, G.C.3
-
16
-
-
0033578381
-
Applications of the self-organising feature map neural network in community data analysis
-
Foody, G.: Applications of the self-organising feature map neural network in community data analysis, Ecol. Model., 120, 97-107, 1999.
-
(1999)
Ecol. Model
, vol.120
, pp. 97-107
-
-
Foody, G.1
-
17
-
-
0030702721
-
Gauss-Newton approximation to Bayesian learning
-
New York
-
Foresee, F. D. and Hagan, M. T.: Gauss-Newton approximation to Bayesian learning, IEEE IJCNN, New York, 3, 1930-1935, 1997.
-
(1930)
IEEE IJCNN
, vol.3
, pp. 1997
-
-
Foresee, F.D.1
Hagan, M.T.2
-
18
-
-
0031998129
-
Application example of neural networks for time series analysis: Rainfall-runoff modelling
-
Furundzic, D.: Application example of neural networks for time series analysis: rainfall-runoff modelling, Signal Process., 64, 383-396, 1998.
-
(1998)
Signal Process
, vol.64
, pp. 383-396
-
-
Furundzic, D.1
-
19
-
-
4143139874
-
Towards the characterization of streamflow simulation uncertainty through multimodel ensembles
-
Georgakakos, K. P., Seo, D., Gupta, H., Schaake, J., and Butts, M. B.: Towards the characterization of streamflow simulation uncertainty through multimodel ensembles, J. Hydrol., 298, 222-241, 2004.
-
(2004)
J. Hydrol
, vol.298
, pp. 222-241
-
-
Georgakakos, K.P.1
Seo, D.2
Gupta, H.3
Schaake, J.4
Butts, M.B.5
-
20
-
-
35448991280
-
Hydrologic data exploration and river flow forecasting of a humid tropical river basin using artificial neural networks
-
Gopakumar R., Takara, K., and James E. J.: Hydrologic Data Exploration and River Flow Forecasting of a Humid Tropical River Basin Using Artificial Neural Networks, Water Resour. Manag., 21(11), 1915-1940, 2007.
-
(2007)
Water Resour. Manag.
, vol.21
, Issue.11
, pp. 1915-1940
-
-
Gopakumar, R.1
Takara, K.2
James, E.J.3
-
21
-
-
0031922634
-
Toward improved calibration of hydrologic models: Multiple and noncommensurable measures of information
-
Gupta, H. V., Sorooshian, S., and Yapo, P. O.: Toward improved calibration of hydrologic models: Multiple and noncommensurable measures of information,Water Resour. Res., 34, 751-764, 1998.
-
(1998)
Water Resour. Res.
, vol.34
, pp. 751-764
-
-
Gupta, H.V.1
Sorooshian, S.2
Yapo, P.O.3
-
22
-
-
0028543366
-
Training feedforward networks with the Marquardt algorithm
-
Hagan, M. T. and Menhaj, M.: Training feedforward networks with the Marquardt algorithm, IEEET. Neural Networ., 5(6), 989-993, 1994.
-
(1994)
IEEET. Neural Networ
, vol.5
, Issue.6
, pp. 989-993
-
-
Hagan, M.T.1
Menhaj, M.2
-
23
-
-
0242475332
-
An unsupervised hierarchical dynamic selforganizing approach to cancer class discovery and marker gene identification in microarray data (supplementary information on "dynamic SOM with hexagonal structure for data mining"
-
Hsu, A. L. and Halgamuge, S. K.: An unsupervised hierarchical dynamic selforganizing approach to cancer class discovery and marker gene identification in microarray data (supplementary information on "Dynamic SOM with hexagonal structure for data mining"), Bioinformatics, 19(16), 2131-2140, 2003.
-
(2003)
Bioinformatics
, vol.19
, Issue.16
, pp. 2131-2140
-
-
Hsu, A.L.1
Halgamuge, S.K.2
-
24
-
-
0036998831
-
Selforganizing linear output map (SOLO): An artificial neural network suitable for hydrologic modeling and analysis
-
doi:10.1029/2001WR000795
-
Hsu, K., Gupta, H. V., Gao, X., Sorooshian, S., and Imam, B.: Selforganizing linear output map (SOLO): An artificial neural network suitable for hydrologic modeling and analysis, Water Resour. Res., 38(12), 1302, doi:10.1029/2001WR000795, 2002.
-
(2002)
Water Resour. Res
, vol.38
, Issue.12
, pp. 1302
-
-
Hsu, K.1
Gupta, H.V.2
Gao, X.3
Sorooshian, S.4
Imam, B.5
-
25
-
-
1542287371
-
Identification of physical processes inherent in artificial neural network rainfall runoff models
-
Jain, A., Sudheer, K. P., and Srinivasulu, S.: Identification of physical processes inherent in artificial neural network rainfall runoff models, Hydrol. Process., 18, 571-581, 2004.
-
(2004)
Hydrol. Process
, vol.18
, pp. 571-581
-
-
Jain, A.1
Sudheer, K.P.2
Srinivasulu, S.3
-
26
-
-
28844473522
-
Integrated approach to model decomposed flow hydrograph using artificial neural network and conceptual techniques
-
Jain, A. and Srinivasulu, S.: Integrated approach to model decomposed flow hydrograph using artificial neural network and conceptual techniques, J. Hydrol., 317, 291-306, 2006.
-
(2006)
J. Hydrol
, vol.317
, pp. 291-306
-
-
Jain, A.1
Srinivasulu, S.2
-
27
-
-
40749144865
-
Review of the selforganizing map (SOM) approach in water resources: Analysis, modelling and application
-
Kalteh, A. M., Hjorth, P., and Berndtsson, R.: Review of the selforganizing map (SOM) approach in water resources: Analysis, modelling and application, Environ. Modell. Softw. 23, 835-845, 2008.
-
(2008)
Environ. Modell. Softw
, vol.23
, pp. 835-845
-
-
Kalteh, A.M.1
Hjorth, P.2
Berndtsson, R.3
-
28
-
-
33748029144
-
Bayesian neural network for rainfall-runoff modeling
-
DOI 10.1029/2005WR003971
-
Khan, M. S. and Coulibaly, P.: Bayesian neural network for rainfall-runoff modeling, Water Resour. Res., 42(7), W07409, doi:10.1029/2005WR003971, 2006. (Pubitemid 44300476)
-
(2006)
Water Resources Research
, vol.42
, Issue.7
-
-
Khan, M.S.1
Coulibaly, P.2
-
29
-
-
0020068152
-
Self-organized formation of topologically correct feature maps
-
Kohonen, T.: Self-organized formation of topologically correct feature maps, Biol. Cybern., 43, 59-69, 1982.
-
(1982)
Biol. Cybern.
, vol.43
, pp. 59-69
-
-
Kohonen, T.1
-
30
-
-
0003410791
-
-
(third edn.), Springer, Berlin, Germany
-
Kohonen, T.: Self-Organizing Maps (third edn.), Springer, Berlin, Germany, 2001.
-
(2001)
Self-Organizing Maps
-
-
Kohonen, T.1
-
31
-
-
0032920124
-
Evaluating the use of 'goodnessof- fit' measures in hydrologic and hydroclimatic model validation
-
Legates, D.R. and McCabe, G.J.: Evaluating the use of 'goodnessof- fit' measures in hydrologic and hydroclimatic model validation, Water Resour. Res., 35, 233-241, 1999.
-
(1999)
Water Resour. Res
, vol.35
, pp. 233-241
-
-
Legates, D.R.1
McCabe, G.J.2
-
32
-
-
0034739246
-
Automatic calibration of a conceptual rainfall-runoff model using multiple objectives
-
Madsen, H.: Automatic calibration of a conceptual rainfall-runoff model using multiple objectives, J. Hydrol., 235, 267-288, 2000.
-
(2000)
J. Hydrol
, vol.235
, pp. 267-288
-
-
Madsen, H.1
-
33
-
-
0033957764
-
Neural networks for the prediction and forecasting of water resources variables: A review of modeling issues and applications
-
Maier, H. and Dandy, G.: Neural networks for the prediction and forecasting of water resources variables: A review of modeling issues and applications, Environ. Modell. Softw., 15(1), 101-104, 2000.
-
(2000)
Environ. Modell. Softw
, vol.15
, Issue.1
, pp. 101-104
-
-
Maier, H.1
Dandy, G.2
-
34
-
-
0030572617
-
A comparison of SOM neural network and hierarchical clustering methods
-
Mangiameli, P., Chen, S. K., and West, D.: A comparison of SOM neural network and hierarchical clustering methods, Eur. J. Oper. Res., 93, 402-417, 1996.
-
(1996)
Eur. J. Oper. Res.
, vol.93
, pp. 402-417
-
-
Mangiameli, P.1
Chen, S.K.2
West, D.3
-
35
-
-
0030159380
-
Artificial neural networks as rainfall runoff models
-
Minns, A. W. and Hall, M. J.: Artificial neural networks as rainfall runoff models, Hydrolog. Sci. J., 41, 399-417, 1996.
-
(1996)
Hydrolog. Sci. J.
, vol.41
, pp. 399-417
-
-
Minns, A.W.1
Hall, M.J.2
-
36
-
-
3142538909
-
Improved streamflow forecasting using self-organizing radial basis function artificial neural networks
-
Moradkhani, H., Hsu, K., Gupta, H. V., and Sorooshian, S: Improved streamflow forecasting using self-organizing radial basis function artificial neural networks, J. Hydrol., 295, 246-262, 2004.
-
(2004)
J. Hydrol.
, vol.295
, pp. 246-262
-
-
Moradkhani, H.1
Hsu, K.2
Gupta, H.V.3
Sorooshian, S.4
-
37
-
-
33845600932
-
Cluster-based hydrologic prediction using genetic-algorithm trained neural networks
-
Parasuraman, K. and Elshorbagy, A.: Cluster-based hydrologic prediction using genetic-algorithm trained neural networks, J. Hydrol. Eng., 12(1), 52-62, 2007.
-
(2007)
J. Hydrol. Eng.
, vol.12
, Issue.1
, pp. 52-62
-
-
Parasuraman, K.1
Elshorbagy, A.2
-
38
-
-
0036698155
-
Comparison of different forms of the multi-layer feed-forward neural network method used for river flow forecasting
-
Shamseldin, A. Y., Nasr, A. E., and O'Connor, K. M.: Comparison of different forms of the Multi-layer Feed-Forward Neural Network method used for river flow forecasting, Hydrol. Earth Syst. Sci., 6, 671-684, 2002, http://www.hydrol-earth-syst-sci.net/6/671/2002/.
-
(2002)
Hydrol. Earth Syst. Sci
, vol.6
, pp. 671-684
-
-
Shamseldin, A.Y.1
Nasr, A.E.2
O'Connor, K.M.3
-
39
-
-
0031259688
-
Methods for combining the outputs of different rainfall-runoff rodels
-
Shamseldin, A. Y., O'Connor, K. M., and Liang, G. C.,: Methods for combining the outputs of different rainfall-runoff rodels, J. Hydrol., 197, 203-229, 1997.
-
(1997)
J. Hydrol.
, vol.197
, pp. 203-229
-
-
Shamseldin, A.Y.1
O'Connor, K.M.2
Liang, G.C.3
-
40
-
-
34848868092
-
A comparative study of three neural network forecast combination methods for simulated river flows of different rainfall-runoff models
-
Shamseldin, A. Y., O'Connor, K. M., and Nasr, A. E.: A comparative study of three neural network forecast combination methods for simulated river flows of different rainfall-runoff models, Hydrolog. Sci. J., 52(5), 896-916, 2007.
-
(2007)
Hydrolog. Sci. J.
, vol.52
, Issue.5
, pp. 896-916
-
-
Shamseldin, A.Y.1
O'Connor, K.M.2
Nasr, A.E.3
-
41
-
-
33645974258
-
Natural phenomena
-
natural phenomena, Neural Networks, 19, 215-224, 2006.
-
(2006)
Neural Networks
, vol.19
, pp. 215-224
-
-
-
42
-
-
33646175957
-
A SOM based 2500-isolated-farsi- word speech recognizer
-
ICANN 2005, edited by: Duch, W., Kacprzyk, J., Oja, E., and Zadrony, S., Springer- Verlag Berlin Heidelberg
-
Shirazi, J. and Menhaj, M. B.: A SOM Based 2500-Isolated-Farsi- Word Speech Recognizer, in: ICANN 2005, LNCS 3696, edited by: Duch, W., Kacprzyk, J., Oja, E., and Zadrony, S., Springer- Verlag Berlin Heidelberg, 589-595, 2005.
-
(2005)
LNCS
, vol.3696
, pp. 589-595
-
-
Shirazi, J.1
Menhaj, M.B.2
-
44
-
-
0037565156
-
Model trees as an alternative to neural networks in rainfall-runoff modeling
-
Solomatine, D. P. and Dulal, K. N.: Model trees as an alternative to neural networks in rainfall-runoff modelling, Hydrolog. Sci. J., 48(3), 399-411, 2003.
-
(2003)
Hydrolog. Sci. J.
, vol.48
, Issue.3
, pp. 399-411
-
-
Solomatine, D.P.1
Dulal, K.N.2
-
45
-
-
36749007877
-
A simplified approach to quantifying predictive and parametric uncertainty in artificial neural network hydrologic models
-
DOI 10.1029/2006WR005352
-
Srivastav, R. K., Sudheer, K. P., and Chaubey, I.: A simplified approach to quantifying predictive and parametric uncertainty in artificial neural network hydrologic models, Water Resour. Res., 23(10), W10407, doi:10.1029/2006WR005352, 2007. (Pubitemid 350210156)
-
(2007)
Water Resources Research
, vol.43
, Issue.10
-
-
Srivastav, R.K.1
Sudheer, K.P.2
Chaubey, I.3
-
46
-
-
33845307599
-
How effective and efficient are multiobjective evolutionary algorithms at hydrologic model calibration?
-
Tang, Y., Reed, P., and Wagener, T.: How effective and efficient are multiobjective evolutionary algorithms at hydrologic model calibration?, Hydrol. Earth Syst. Sci., 10, 289-307, 2006, http://www.hydrol-earth-syst-sci.net/10/ 289/2006/.
-
(2006)
Hydrol. Earth Syst. Sci
, vol.10
, pp. 289-307
-
-
Tang, Y.1
Reed, P.2
Wagener, T.3
-
47
-
-
37549066943
-
Multistep ahead streamflow forecasting: Role of calibration data in conceptual and neural network modeling
-
doi:10.1029/2006WR005383
-
Toth, E. and Brath, A.: Multistep ahead streamflow forecasting: Role of calibration data in conceptual and neural network modeling, Water Resour. Res., 43, W11405, doi:10.1029/2006WR005383, 2007.
-
(2007)
Water Resour. Res.
, vol.43
-
-
Toth, E.1
Brath, A.2
-
48
-
-
0030298951
-
Combining Kohonen maps with ARIMA time series models to forecast traffic flow
-
Van der Voort, M., Dougherty, M., and Watson, S.: Combining Kohonen maps with ARIMA time series models to forecast traffic flow, Transport. Res., C4(5), 307-318, 1996.
-
(1996)
Transport. Res
, vol.C4
, Issue.5
, pp. 307-318
-
-
Van Der Voort, M.1
Dougherty, M.2
Watson, S.3
-
49
-
-
1542442237
-
Effective and efficient algorithm for multiobjective optimization of hydrologic models
-
Vrugt, J. A., Gupta, H. V., Bastidas, L. A., Bouten, W., and Sorooshian, S.: Effective and efficient algorithm for multiobjective optimization of hydrologic models, Water Resour. Res., 39, 1-19, 2003.
-
(2003)
Water Resour. Res.
, vol.39
, pp. 1-19
-
-
Vrugt, J.A.1
Gupta, H.V.2
Bastidas, L.A.3
Bouten, W.4
Sorooshian, S.5
-
50
-
-
33646547633
-
Forecasting daily streamflow using hybrid ANN models
-
Wang, W., Gelder, P., Vrijling, J. K., and Ma, J.: Forecasting daily streamflow using hybrid ANN models, J. Hydrol., 324(1), 383-399, 2006.
-
(2006)
J. Hydrol
, vol.324
, Issue.1
, pp. 383-399
-
-
Wang, W.1
Gelder, P.2
Vrijling, J.K.3
Ma, J.4
-
51
-
-
0002919951
-
Progress and directions in rainfall-runoff modeling
-
edited by: Jakeman, A. J., Beck, M. B., and McAleer, M. J., Wiley, Chichester
-
Wheater, H. S., Jakeman, A. J., and Beven, K. J.: Progress and directions in rainfall-runoff modelling, in: Modelling change in environmental systems, edited by: Jakeman, A. J., Beck, M. B., and McAleer, M. J., Wiley, Chichester, 101-132, 1993.
-
(1993)
Modelling Change in Environmental Systems
, pp. 101-132
-
-
Wheater, H.S.1
Jakeman, A.J.2
Beven, K.J.3
-
53
-
-
0037099572
-
Advances in real-time flood forecasting
-
Young, P. C.: Advances in real-time flood forecasting, Philos. T. R. Soc. Lond., 360, 1433-1450, 2002.
-
(2002)
Philos. T. R. Soc. Lond.
, vol.360
, pp. 1433-1450
-
-
Young, P.C.1
-
54
-
-
0034100712
-
Prediction of watershed runoff using bayesian concepts and modular neural networks
-
Zhang, B. and Govindaraju, S.: Prediction of Watershed Runoff using Bayesian Concepts and Modular Neural Networks, Water Resour. Res., 36(3), 753-762, 2000.
-
(2000)
Water Resour. Res.
, vol.36
, Issue.3
, pp. 753-762
-
-
Zhang, B.1
Govindaraju, S.2
|