Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting

Progress in Physical Geography

Volumn 36, Issue 4, 2012, Pages 480-513

  Abrahart, Robert J ^a   Anctil, François ^b   Coulibaly, Paulin ^c   Dawson, Christian W ^d   Mount, Nick J ^a   See, Linda M ^e   Shamseldin, Asaad Y ^f   Solomatine, Dimitri P ^g   Toth, Elena ^h   Wilby, Robert L ^d  

^a UNIVERSITY OF NOTTINGHAM   (United Kingdom)

^b UNIVERSITÉ LAVAL   (Canada)

^c MCMASTER UNIVERSITY   (Canada)

^d LOUGHBOROUGH UNIVERSITY   (United Kingdom)

^e INTERNATIONAL INSTITUTE FOR APPLIED SYSTEMS ANALYSIS   (Austria)

^f UNIVERSITY OF AUCKLAND   (New Zealand)

^g DELFT UNIVERSITY OF TECHNOLOGY   (Netherlands)

^h UNIVERSITY OF BOLOGNA   (Italy)

Author keywords

forecasting; modelling; network; neural; river

Indexed keywords

ARTIFICIAL NEURAL NETWORK; BENCHMARKING; DATA SET; DRAINAGE NETWORK; FORECASTING METHOD; HYDROLOGICAL MODELING; PARADIGM SHIFT; RAINFALL-RUNOFF MODELING; RIVER FLOW; STREAMFLOW;

EID: 84863764389     PISSN: 03091333     EISSN: None     Source Type: Journal    
DOI: 10.1177/0309133312444943     Document Type: Article

References (279)

1. Abrahart RJ,See LM.Comparing neural network and autoregressive moving average techniques for the provision of continuous river flow forecasts in two contrasting catchments.Hydrological Processes. 2000;14:2157-2172.
2. Abrahart RJ,See LM.Multi-model data fusion for river flow forecasting: An evaluation of six alternative methods based on two contrasting catchments.Hydrology and Earth System Sciences. 2002;6:655-670.
3. Abrahart RJ,See LM.Neural network modelling of non-linear hydrological relationships.Hydrology and Earth System Sciences. 2007;11:1563-1579.
4. Abrahart RJ,Ab Ghani N,Swan J.Discussion of 'An explicit neural network formulation for evapotranspiration'.Hydrological Sciences Journal. 2009;54:382-388.
5. Abrahart RJ,Heppenstall AJ,See LM.Timing error correction procedure applied to neural network rainfall-runoff modelling.Hydrological Sciences Journal. 2007a;52:414-431.
6. Abrahart RJ,Mount NJ,Shamseldin AY.Discussion of 'Reservoir Computing approach to Great Lakes water level forecasting' by P. Coulibaly [J. Hydrol. 381 (2010) 76-88].Journal of Hydrology. 2012a;422-423:76-80.
7. Abrahart RJ,Mount NJ,Shamseldin AY.Neuroemulation: Definition and key benefits for water resources research.Hydrological Sciences Journal. 2012b;57:407-423.
8. Abrahart RJ,See LM,Dawson CWPractical Hydroinformatics: Computational Intelligence and Technological Developments in Water Applications. Abrahart RJSee LMSolomatine DP, ed. Berlin: Springer. Water Science and Technology Library; 2008:33-47.
9. Abrahart RJ,See LM,Heppenstall AJ.Neuroevolution applied to river level forecasting under winter flood and drought conditions.Journal of Intelligent Systems. 2007b;16:373-386.
10. Abrahart RJ,See LM,Kneale PE.Using pruning algorithms and genetic algorithms to optimise network architectures and forecasting inputs in a neural network rainfall-runoff model.Journal of Hydroinformatics. 1999;1:103-114.
11. Abrahart RJ,Dawson CW,See LM, et al.Discussion of 'Evapotranspiration modelling using support vector machines'.Hydrological Sciences Journal. 2010a;55:1442-1450.
12. Abrahart RJ,See LM,Dawson CW, et alAdvances in Data-Based Approaches for Hydrologic Modeling and Forecasting. Sivakumar BBerndtsson R, ed. Hackensack, NJ: World Scientific Publishing; 2010b:267-346.
13. Abu-Mostafa Y.Hints.Neural Computation. 1995;7:639-671.
14. Adamowski JF.River flow forecasting using wavelet and cross-wavelet transform models.Hydrological Processes. 2008a;22:4877-4891.
15. Adamowski JF.Development of a short-term river flood forecasting method for snowmelt driven floods based on wavelet and cross-wavelet analysis.Journal of Hydrology. 2008b;353:247-266.
16. Adamowski J,Sun K.Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds.Journal of Hydrology. 2010;390:85-91.
17. Ahmad S,Simonovic SP.An artificial neural network model for generating hydrograph from hydro-meteorological parameters.Journal of Hydrology. 2005;315:236-251.
18. Ahmad S,Simonovic SP.An intelligent decision support system for management of flood.Water Resources Management. 2006;20:391-410.
19. Aksoy H,Güven A,Aytek A, et al.Discussion of 'Generalized regression neural networks for evapotranspiration modelling' by O. Kişi.Hydrological Sciences Journal. 2007;52:825-828.
20. Aksoy H,Güven A,Aytek A, et al.Comment on Kişi O. 2007 'Evapotranspiration modelling from climatic data using neural network computing technique'.Hydrological Processes. 2008;22:2715-2717.
21. Alvisi S,Franchini M.Fuzzy neural networks for water level and discharge forecasting with uncertainty.Environmental Modelling and Software. 2011;26:523-537.
22. Artificial neural networks in hydrology. I: Preliminary concepts.ASCE Journal of Hydrologic Engineering. 2000a;5:115-123.
23. Artificial neural networks in hydrology. II: Hydrologic applications.ASCE Journal of Hydrologic Engineering. 2000b;5:124-137.
24. Anctil F,Lauzon N.Generalisation for neural networks through data sampling and training procedures, with applications to streamflow predictions.Hydrology and Earth System Sciences. 2004;8:940-958.
25. Anctil F,Tape DG.An exploration of artificial neural network rainfall-runoff forecasting combined with wavelet decomposition.Journal of Environmental Engineering and Science. 2004;3 (S1): S121-S128.
26. Anctil F,Lauzon N,Filion M.Added gains of soil moisture content observations for streamflow predictions using neural networks.Journal of Hydrology. 2008;359:225-234.
27. Anctil F,Michel C,Perrin C,Andreassian V.A soil moisture index as an auxiliary ANN input for stream flow forecasting.Journal of Hydrology. 2004a;286:155-167.
28. Anctil F,Perrin C,Andreassian V.ANN output updating of lumped conceptual rainfall/runoff forecasting models.Journal of the American Water Resources Association. 2003;39:1269-1279.
29. Anctil F,Perrin C,Andreassian V.Impact of the length of observed records on the performance of ANN and of conceptual parsimonious rainfall-runoff forecasting models.Environmental Modelling and Software. 2004b;19:357-368.
30. Antar MA,Elassiouti I,Allam MN.Rainfall-runoff modelling using artificial neural networks technique: A Blue Nile catchment case study.Hydrological Processes. 2006;20:1201-1216.
31. Aqil M,Kita I,Yano A,Nishiyama S.A comparative study of artificial neural networks and neuro-fuzzy in continuous modeling of the daily and hourly behaviour of runoff.Journal of Hydrology. 2007;337:22-34.
32. Proceedings Eighth National Hydrology Symposium. University of Birmingham, UK:; 2002:75-81.
33. Asefa T.Ensemble streamflow forecast: A GLUE-based neural network approach.Journal of the American Water Resources Association. 2009;45:1155-1163.
34. Azmi M,Araghinejad S,Kholghi M.Multi model data fusion for hydrological forecasting using K-nearest neighbor method.Iranian Journal of Science and Technology, Transaction B, Engineering. 2010;34:81-92.
35. Babovic V.Data mining in hydrology.Hydrological Processes. 2005;19:1511-1515.
36. Bakkalbasi N,Bauer K,Glover J,Wang L.Three options for citation tracking: Google Scholar, Scopus and Web of Science.Biomedical Digital Libraries 3. 7. 2006;:.
37. Baratti R,Cannas B,Fanni A,Pintus M,Sechi GM,Toreno N, et al.River flow forecast for reservoir management through neural networks.Neurocomputing. 2003;55:421-437.
38. Behzad M,Asghari K,Eazi M,Palhang M.Generalization performance of support vector machines and neural networks in runoff modelling.Expert Systems with Applications. 2009;36:7624-7629.
39. Bergström S,Forsman A.Development of a conceptual deterministic rainfall-runoff model.Nordic Hydrology. 1973;4:147-170.
40. Besaw LE,Rizzo DM,Bierman PR,Hackett WR.Advances in ungauged streamflow prediction using artificial neural networks.Journal of Hydrology. 2010;386:27-37.
41. Boucher MA,Laliberté JP,Anctil F.An experiment on the evolution of an ensemble of neural networks for streamflow forecasting.Hydrology and Earth System Sciences. 2010;14:603-612.
42. Boucher MA,Perreault L,Anctil F.Tools for the assessment of hydrological ensemble forecasts obtained by neural networks.Journal of Hydroinformatics. 2009;11:297-307.
43. Bowden GJ,Dandy GC,Maier HR.Data transformation for neural network models in water resources applications.Journal of Hydroinformatics. 2003;5:245-258.
44. Bowden GJ,Dandy GC,Maier HR.Input determination for neural network models in water resources applications. Part 1 - background and methodology.Journal of Hydrology. 2005a;301:75-92.
45. Bowden GJ,Maier HR,Dandy GC.Optimal division of data for neural network models in water resources applications.Water Resources Research. 2002;38:1010.
46. Bowden GJ,Maier HR,Dandy GC.Input determination for neural network models in water resources applications. Part 2 - Case study: Forecasting salinity in a river.Journal of Hydrology. 2005b;301:93-107.
47. Brath A,Montanari A,Toth E.Neural networks and non-parametric methods for improving realtime flood forecasting through conceptual hydrological models.Hydrology and Earth System Sciences. 2002;6:627-640.
48. Breiman L.Stacked regressor.Machine Learning. 1996;24:49-64.
49. Campolo M,Andreussi P,Soldati A.River flood forecasting with a neural network model.Water Resources Research. 1999;35:1191-1197.
50. Cannas B,Fanni A,See L,Sias G.Data preprocessing for river flow forecasting using neural networks: Wavelet transforms and data partitioning.Physics and Chemistry of the Earth. 2006;31:1164-1171.
51. Carcano EC,Bartolini P,Muselli MDevice Applications of Nonlinear Dynamics. Baglio SBulsara A, ed. Berlin: Springer; 2006:191-200.
52. Carcano EC,Bartolini P,Muselli M,Piroddi L.Jordan recurrent neural network versus IHACRES in modelling daily streamflows.Journal of Hydrology. 2008;362:291-307.
53. Carreau J,Naveau P,Sauquet E.A statistical rainfall-runoff mixture model with heavy-tailed components.Water Resources Research. 2009;45:W10437.
54. Chang F-J,Chen Y-C.A counterpropagation fuzzy-neural network modeling approach to real time streamflow prediction.Journal of Hydrology. 2001;245:153-164.
55. Chang F-J,Chang K-Y,Chang L-C.Counterpropagation fuzzy-neural network for city flood control system.Journal of Hydrology. 2008;358:24-34.
56. Chang F-J,Chang L-C,Huang HL.Real-time recurrent learning neural network for stream-flow forecasting.Hydrological Processes. 2002;16:2577-2588.
57. Chang F-J,Chang L-C,Wang Y-S.Enforced self-organizing map neural networks for river flood forecasting.Hydrological Processes. 2007;21:741-749.
58. Chang F-J,Hu H-F,Chen Y-C.Counterpropagation fuzzy-neural network for streamflow reconstruction.Hydrological Processes. 2001;15:219-232.
59. Chang L-C,Chang F-J,Chiang Y-M.A two-step-ahead recurrent neural network for stream-flow forecasting.Hydrological Processes. 2004;18:81-92.
60. Chang L-C,Chang F-J,Wang Y-P.Auto-configuring radial basis function networks for chaotic time series and flood forecasting.Hydrological Processes. 2009;23:2450-2459.
61. Chau KW.Particle swarm optimization training algorithm for ANNs in stage prediction of Shing Mun River.Journal of Hydrology. 2006;329:363-367.
62. Chau KW.A split-step particle swarm optimization algorithm in river stage forecasting.Journal of Hydrology. 2007;346:131-135.
63. Chau KW,Wu CL,Li Y-S.Comparison of several flood forecasting models in the Yangtze River.ASCE Journal of Hydrologic Engineering. 2005;10:485-491.
64. Chen C-S,Chen BP-T,Chou FN-F,Yang C-C.Development and application of a decision group Back-Propagation Neural Network for flood forecasting.Journal of Hydrology. 2010a;385:173-182.
65. Chen C-S,Chou FN-F,Chen BP-T.Spatial information-based back-propagation neural network modeling for outflow estimation of ungauged catchment.Water Resources Management. 2010b;24:4175-4197.
66. Chen J,Adams BJ.Integration of artificial neural networks with conceptual models in rainfall-runoff modeling.Journal of Hydrology. 2006a;318:232-249.
67. Chen J,Adams BJ.Semidistributed form of the tank model coupled with artificial neural networks.ASCE Journal of Hydrologic Engineering. 2006b;11:408-417.
68. Chen S-H,Lin Y-H,Chang L-C,Chang F-J.The strategy of building a flood forecast model by neuro-fuzzy method.Hydrological Processes. 2006;20:1525-1540.
69. Chen Y-H,Chang F-J.Evolutionary artificial neural networks for hydrological systems forecasting.Journal of Hydrology. 2009;367:125-137.
70. Chiang Y-M,Chang LC,Chang FJ.Comparison of static-feedforward and dynamic feedback neural networks for rainfall-runoff modeling.Journal of Hydrology. 2004;290:297-311.
71. Chidthong Y,Tanaka H,Supharatid S.Developing a hybrid multi-model for peak flood forecasting.Hydrological Processes. 2009;23:1725-1738.
72. Chua LHC,Wong TSW.Improving event-based rainfall-runoff modeling using a combined artificial neural network-kinematic wave approach.Journal of Hydrology. 2010;390:92-107.
73. Corani G,Guariso G.An application of pruning in the design of neural networks for real time flood forecasting.Neural Computing and Applications. 2005;14:66-77.
74. Corzo G,Solomatine D.Baseflow separation techniques for modular artificial neural networks modelling in flow forecasting.Hydrological Sciences Journal. 2007a;52:491-507.
75. Corzo G,Solomatine D.Knowledge-based modularization and global optimization of artificial neural network models in hydrological forecasting.Neural Networks. 2007b;20:528-536.
76. Corzo GA,Solomatine DP,Hidayat, et al.Combining semi-distributed process-based and data-driven models in flow simulation: A case study of the Meuse river basin.Hydrology and Earth System Sciences. 2009;13:1619-1634.
77. Coulibaly P.Reservoir computing approach to Great Lakes water level forecasting.Journal of Hydrology. 2010;381:76-88.
78. Coulibaly P,Anctil F,Bobée B.Hydrological forecasting using artificial neural networks: The state of the art (in French).Canadian Journal of Civil Engineering. 1999;26:293-304.
79. Coulibaly P,Anctil F,Bobée B.Daily reservoir inflow forecasting using artificial neural networks with stopped training approach.Journal of Hydrology. 2000;230:244-257.
80. Coulibaly P,Anctil F,Bobée B.Multivariate reservoir inflow forecasting using temporal neural networks.ASCE Journal of Hydrologic Engineering. 2001a;6:367-376.
81. Coulibaly P,Anctil B,Bobée B.Improving extreme hydrologic events forecasting using a new criterion for ANN selection.Hydrological Processes. 2001b;15:1533-1536.
82. Dastorani MT,Moghadamnia A,Piri J,Rico-Ramirez M.Application of ANN and ANFIS models for reconstructing missing flow data.Environmental Monitoring and Assessment. 2010;166:421-434.
83. Dawson CW,Wilby R.An artificial neural network approach to rainfall-runoff modelling.Hydrological Sciences Journal. 1998;43:47-65.
84. Dawson CW,Wilby RL.Hydrological modelling using artificial neural networks.Progress in Physical Geography. 2001;25:80-108.
85. Dawson CW,Abrahart RJ,See LM.HydroTest: A web-based toolbox of evaluation metrics for the standardised assessment of hydrological forecasts.Environmental Modelling and Software. 2007;22:1034-1052.
86. Dawson CW,Abrahart RJ,See LM.HydroTest: Further development of a web resource for the standardised assessment of hydrological models.Environmental Modelling and Software. 2010;25:1481-1482.
87. Dawson CW,Harpham C,Wilby RL,Chen Y.An evaluation of artificial neural network techniques for flow forecasting in the River Yangtze, China.Hydrology and Earth Systems Sciences. 2002;6:619-626.
88. Dawson CW,See LM,Abrahart RJ,Heppenstall AJ.Symbiotic adaptive neuro-evolution applied to rainfall-runoff modelling in northern England.Neural Networks. 2006;19:236-247.
89. Dawson CW,See LM,Abrahart RJ, et alIJCNN2005: Proceedings International Joint Conference on Neural Networks. 2005:2666-2670.
90. Deb K,Pratap A,Agarwal S,Meyarivan T.A fast and elitist multiobjective genetic algorithm: NSGA-II.IEEE Transactions on Evolutionary Computation. 2002;6:182-197.
91. De Vos NJ,Rientjes THM.Multi-objective performance comparison of an artificial neural network and a conceptual rainfall-runoff model.Hydrological Sciences Journal. 2007;52:397-413.
92. De Vos NJ,Rientjes THM.Multi-objective training of artificial neural networks for rainfall-runoff modelling.Water Resources Research. 2008;44:W08434.
93. Dibike YB,Coulibaly P.Temporal neural networks for downscaling climate variability and extremes.Neural Networks. 2006;19:135-144.
94. Dibike YB,Velickov S,Solomatine DP,Abbott MB.Model induction with support vector machines: Introduction and applications.ASCE Journal of Computing in Civil Engineering. 2001;15:208-216.
95. Domínguez E,Dawson CW,Ramírez A,Abrahart RJ.The search for orthogonal hydrological modelling metrics: A case study of 20 monitoring stations in Colombia.Journal of Hydroinformatics. 2011;13:429-442.
96. El-Shafie A,Taha MR,Noureldin A.A neuro-fuzzy model for inflow forecasting of the Nile river at Aswan high dam.Water Resources Management. 2007;21:533-556.
97. Elshorbagy A,Corzo G,Srinivasulu S,Solomatine DP.Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - Part 1: Concepts and methodology.Hydrology and Earth System Sciences. 2010a;14:1931-1941.
98. Elshorbagy A,Corzo G,Srinivasulu S,Solomatine DP.Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - Part 2: Application.Hydrology and Earth System Sciences. 2010b;14:1943-1961.
99. Fernando DAK,Shamseldin AY.Investigation of internal functioning of the radial-basis-function neural network river flow forecasting models.ASCE Journal of Hydrologic Engineering. 2009;14:286-292.
100. Firat M.Comparison of artificial intelligence techniques for river flow forecasting.Hydrology and Earth System Sciences. 2008;12:123-139.
101. Firat M,Güngör M.River flow estimation using adaptive neuro fuzzy inference system.Mathematics and Computers in Simulation. 2007;75:87-96.
102. Firat M,Güngör M.Hydrological time-series modelling using an adaptive neuro-fuzzy inference system.Hydrological Processes. 2008;22:2122-2132.
103. Foresee FD,Hagan MT.Gauss-Newton approximation to Bayesian learning. Proceedings of the International Joint Conference on Neural Networks; 1997Houston, Texas; 1997. 1930.
104. Freer J,Beven K,Ambroise B.Bayesian estimation of uncertainty in runoff prediction and the value of data: An application of the GLUE approach.Water Resources Research. 1996;32:2161-2173.
105. Freund Y,Schapire R.A decision-theoretic generalisation of on-line learning and an application of boosting.Journal of Computer and System Sciences. 1997;55:119-139.
106. Friedman JH.Multivariate adaptive regression splines.Annals of Statistics. 1991;19:1-67.
107. Garcia LA,Shigidi A.Using neural networks for parameter estimation in ground water.Journal of Hydrology. 2006;318:215-231.
108. Gaume E,Gosset R.Over-parameterisation, a major obstacle to the use of artificial neural networks in hydrology?.Hydrology and Earth Systems Sciences. 2003;7:693-706.
109. Gautam DK,Holz KP.Rainfall-runoff modelling using adaptive neuro-fuzzy systems.Journal of Hydroinformatics. 2001;3:3-10.
110. Giustolisi O,Laucelli D.Improving generalization of artificial neural networks in rainfall-runoff modelling.Hydrological Sciences Journal. 2005;50:439-457.
111. Goswami M,O'Connor KM,Bhattarai KP,Shamseldin AY.Assessing the performance of eight real-time updating models and procedures for the Brosna River.Hydrology and Earth System Sciences. 2005;9:394-411.
112. Guo S,Zhang H,Chen H,Peng D,Liu P,Pang B.A reservoir flood forecasting and control system for China.Hydrological Sciences Journal. 2004;49:959-972.
113. Guo W,Wang H,Xu J,Zhang YProceedings of 2010 International Conference on Intelligent Computation Technology and Automation. 2010:986-971.
114. Hagan MT,Menhaj MB.Training feed-forward networks with the Marquardt algorithm.IEEE Transactions on Neural Networks. 1994;5:989-993.
115. Halff AH,Halff HM,Azmoodeh MEngineering Hydrology (1993): Proceedings of Symposium sponsored by Hydraulics Division of the American Society of Civil Engineers. Kuo CY, ed. New York: American Society of Civil Engineers; 1993:760-765.
116. Hall MJ,Minns AWProceedings 4th National Hydrology Symposium. London: British Hydrological Society; 1993:5.51-5.57.
117. Hashem SProceedings of the 1992 International Joint Conference on Neural Networks. Baltimore; 1992:419-424.
118. Haykin SNeural Networks: A Comprehensive Foundation. Englewood Cliffs, NJ: Prentice-Hall; 1991:.
119. Heppenstall AJ,See LM,Abrahart RJ,Dawson CWPractical Hydroinformatics: Computational Intelligence and Technological Developments in Water Applications. Abrahart RJSee LMSolomatine DP, ed. Berlin: Springer; 2008:319-330.
120. Hestenes MR,Stiefel EL.Methods of conjugate gradients for solving linear systems.Journal of Research of the National Bureau of Standards, Section B: Mathematical Sciences. 1952;49:409-436.
121. Hjelmfelt AT,Wang MEngineering Hydrology (1993). Kuo CY, ed. New York: American Society of Civil Engineers; 1993:756-759.
122. Holländer HM,Blume T,Bormann H, et al.Comparative predictions of discharge from an artificial catchment (Chicken Creek) using sparse data.Hydrology and Earth System Sciences. 2009;13:2069-2094.
123. Hong Y-ST,White PA.Hydrological modeling using a dynamic neuro-fuzzy system with on-line and local learning algorithm.Advances in Water Resources. 2009;32:110-119.
124. Horn J,Goldberg DE,Deb K.Implicit niching in a learning classifier system: Nature's way.Evolutionary Computation. 1994;2:27-66.
125. Hsu K-L,Gupta HV,Gao X, et al.Self-organizing linear output map (SOLO): An artificial neural network suitable for hydrologic modeling and analysis.Water Resources Research. 2002;38:1302.
126. Hsu K-L,Gupta HV,Sorooshian S.Artificial neural network modeling of the rainfall-runoff process.Water Resources Research. 1995;31:2517-2530.
127. Hsu M-H,Lin S-H,Fu J-C, et al.Longitudinal stage profiles forecasting in rivers for flash floods.Journal of Hydrology. 2010;388:426-437.
128. Hu T,Wu F,Zhang X.Rainfall-runoff modeling using principal component analysis and neural network.Nordic Hydrology. 2007;38:235-248.
129. Hu TS,Lam KC,Ng ST.River flow time series prediction with a range-dependent neural network.Hydrological Sciences Journal. 2001;46:729-745.
130. Huang W,Xu B,Chan-Hilton A.Forecasting flows in Apalachicola River using neural networks.Hydrological Processes. 2004;18:2545-2564.
131. Imrie CE,Durucan S,Korre A.River flow prediction using artificial neural networks: Generalisation beyond the calibration range.Journal of Hydrology. 2000;233:138-153.
132. Ivakhnenko AG.Polynomial theory of complex system. IEEE Transactions on Systems.Man and Cybernetics. 1971;SMCI-1:364-378.
133. Jacquin AP,Shamseldin AY.Review of the application of fuzzy inference systems in river flow forecasting.Journal of Hydroinformatics. 2009;11:202-210.
134. Jaeger H.The 'echo state' approach to analysing and training recurrent neural networks.German National Research Center for Information Technology, GMD Report 148. 2001;:.
135. Jain A,Kumar AM.Hybrid neural network models for hydrologic time series forecasting.Applied Soft Computing. 2007;7:585-592.
136. Jain A,Kumar S.Dissection of trained neural network hydrologic models for knowledge extraction.Water Resources Research. 2009;45:W07420.
137. Jain A,Ormsbee LE.A decision support system for drought characterization and management.Civil Engineering and Environmental Systems. 2001;18:105-140.
138. Jain A,Srinivasulu S.Development of effective and efficient rainfall-runoff models using integration of deterministic, real-coded genetic algorithms and artificial neural network techniques.Water Resources Research. 2004;40:W04302.
139. Jain A,Srinivasulu S.Integrated approach to model decomposed flow hydrograph using artificial neural network and physically techniques.Journal of Hydrology. 2006;317:291-306.
140. Jain A,Maier HR,Dandy GC,Sudheer KP.Rainfall runoff modelling using neural networks: State-of-the-art and future research needs.Indian Society for Hydraulics Journal of Hydraulic Engineering. 2009;15:52-74.
141. Jain A,Sudheer KP,Srinivasulu S.Identification of physical processes inherent in artificial neural network rainfall runoff models.Hydrological Processes. 2004;18:571-581.
142. Jang J-SR.ANFIS: Adaptive-Network-based Fuzzy Inference System.IEEE Transactions on Systems, Man and Cybernetics. 1993;23:665-685.
143. Jeong D-I,Kim Y-O.Rainfall-runoff models using artificial neural networks for ensemble streamflow prediction.Hydrological Processes. 2005;19:3819-3835.
144. Jeong D-I,Kim Y-O.Combining single-value streamflow forecasts - a review and guidelines for selecting techniques.Journal of Hydrology. 2009;377:284-299.
145. Kagoda PA,Ndiritu J,Ntuli C,Mwakab B.Application of radial basis function neural networks to short-term streamflow forecasting.Physics and Chemistry of the Earth. 2010;35:571-581.
146. Kamp RG,Savenije HHG.Optimising training data for ANNs with genetic Algorithms.Hydrology and Earth System Sciences. 2006;10:603-608.
147. Kamp RG,Savenije HHG.Hydrological model coupling with ANNs.Hydrology and Earth System Sciences. 2007;11:1869-1881.
148. Kang KW,Park CY,Kim JH.Neural network and its application to rainfall-runoff forecasting.Korean Journal of Hydrosciences. 1993;4:1-9.
149. Karunanithi N,Grenney WJ,Whitley D,Bovee K.Neural networks for river flow prediction.Journal of Computing in Civil Engineering. 1994;8:201-220.
150. Keskin ME,Taylan D.Artificial models for interbasin flow prediction in southern Turkey.ASCE Journal of Hydrologic Engineering. 2009;14:752-758.
151. Keskin ME,Taylan D,Terzi O.Adaptive neural-based fuzzy inference system (ANFIS) approach for modelling hydrological time series.Hydrological Sciences Journal. 2006;51:588-598.
152. Khan MS,Coulibaly P.Bayesian neural network for rainfall-runoff modeling.Water Resources Research. 2006;42:W07409.
153. Kingston GB,Maier HR,Lambert MF.Calibration and validation of neural networks to ensure physically plausible hydrological modelling.Journal of Hydrology. 2005;314:158-176.
154. Kingston GB,Maier HR,Lambert MF.A probabilistic method to assist knowledge extraction from artificial neural networks used for hydrological prediction.Mathematical and Computer Modelling. 2006;44:499-512.
155. Kişi Ö.River flow forecasting and estimation using different artificial neural network techniques.Hydrology Research. 2008a;39:27-40.
156. Kişi Ö.Stream flow forecasting using neuro-wavelet technique.Hydrological Processes. 2008b;22:4142-4152.
157. Kişi Ö.Neural networks and wavelet conjunction model for intermittent streamflow forecasting.ASCE Journal of Hydrological Engineering. 2009;14:773-782.
158. Kişi Ö,Cigizoglu HK.Comparison of different ANN techniques in river flow prediction.Civil Engineering and Environmental Systems. 2007;24:211 231.
159. Kohonen T.Self-organized formation of topologically correct feature maps.Biological Cybernetics. 1982;43:59-69.
160. Kohonen TSelf-Organizing Maps. Heidelberg: Springer; 1995:.
161. Koutsoyiannis D.Discussion of 'generalized regression neural networks for evapotranspiration modelling' by O. Kişi.Hydrological Sciences Journal. 2007;52:832-835.
162. Kumar DN,Raju KS,Sathish T.River flow forecasting using recurrent neural networks.Water Resources Management. 2004;18:143-161.
163. Kuok KK,Harun S,Shamsuddin SM.Particle swarm optimization feedforward neural network for modeling runoff.International Journal of Environmental Science and Technology. 2010;7:67-78.
164. Labat D.Recent advances in wavelet analyses: Part 1 - A review of concepts.Journal of Hydrology. 2005;314:275-288.
165. Labat D,Ronchail J,Guyot JL.Recent advances in wavelet analyses: Part 2. - Amazon, Parana, Orinoco and Congo discharges time scale variability.Journal of Hydrology. 2005;314:289-311.
166. Leahy P,Kiely G,Corcoran G.Structural optimisation and input selection of an artificial neural network for river level prediction.Journal of Hydrology. 2008;355:192-201.
167. Levenberg K.A method for the solution of certain non-linear problems in least squares.The Quarterly of Applied Mathematics. 1944;2:164-168.
168. Levine-Clark M,Gil EL.A comparative citation analysis of Web of Science, Scopus, and Google Scholar.Journal of Business and Finance Librarianship. 2009;14:32-46.
169. Liang F,Wong WH.Evolutionary Monte Carlo sampling: Applications to Cp model sampling and change-point problem.Statistica Sinica. 2001;10:317-342.
170. Lin J-Y,Cheng C-T,Chau K-W.Using support vector machines for long-term discharge prediction.Hydrological Sciences Journal. 2006;51:599-612.
171. Lindström G,Gardelin M,Johansson B, et al.Development and test of the distributed HBV-96 hydrological model.Journal of Hydrology. 1997;201:272-288.
172. Liong S-Y,Chan WTProceedings 3 rd International Conference on the Application of Artificial Intelligence to Civil and Structural Engineering. Topping BHVKhan AI, ed. Edinburgh: Civil-Comp Press; 1993:67-70.
173. Liong S-Y,Sivapragasam C.Flood stage forecasting with support vector machines.Journal of the American Water Resources Association. 2002;38:173-186.
174. Liu D,Chen X,Lian Y,Lou Z.Impacts of climate change and human activities on surface runoff in the Dongjiang River basin of China.Hydrological Processes. 2010;24:1487-1495.
175. Lukoševičius M,Jaeger H.Reservoir computing approaches to recurrent neural network training.Computer Science Review. 2009;3:127-149.
176. Maier HR,Dandy GC.Neural networks for the prediction and forecasting of water resources variables: A review of modelling issues and applications.Environmental Modelling and Software. 2000;15:101-123.
177. Maier HR,Jain A,Dandy GC,Sudheer KP.Methods used for the development of neural networks for the prediction of water resources variables: Current status and future directions.Environmental Modelling and Software. 2010;25:891-909.
178. ManusthiparomCApirumanekulCMahaxayM (2005) Flood forecasting and river monitoring system in the Mekong River Basin. In: Proceedings 2nd Southeast Asia Water Forum, Bali, Indonesia, 29 August-3 September. Available at: http://ns1.mrcmekong.org/download/Papers/flood-Forecast-river-Monitor-Sys.pdf.
179. Marquardt D.An algorithm for least-squares estimation of nonlinear parameters.SIAM Journal on Applied Mathematics. 1963;11:431-441.
180. May RJ,Maier HR,Dandy GC.Data splitting for artificial neural networks using SOM-based stratified sampling.Neural Networks. 2010;23:283-294.
181. May R,Maier H,Dandy G,Fernando T.Nonlinear variable selection for artificial neural networks using partial mutual information.Environmental Modelling and Software. 2008;23:1312-1326.
182. Minns AW,Hall MJ.Artificial neural networks as rainfall-runoff models.Hydrological Sciences Journal. 1996;41:399-417.
183. Moller MF.A scaled conjugate gradient algorithm for fast supervised learning.Neural Networks. 1993;6:525-533.
184. Moore GACrossing the Chasm: Marketing and Selling High-Tech Products to Mainstream Customers. New York: HarperCollins Publishers; 1991:.
185. Moreno J,Abudu S,Bawazir AS,King JP.Comment on 'Kişi, Ö. 2009. Daily pan evaporation modelling using multi-layer perceptrons and radial basis neural networks. Hydrological Processes 23: 213-223'.Hydrological Processes. 2010;24:3115-3118.
186. Mount NJ,Abrahart RJ.Discussion of 'River flow estimation from upstream flow records by artificial intelligence methods' by M.E. Turan and M.A. Yurdusev [J. Hydrol. 369 (2009) 71-77]'.Journal of Hydrology. 2011a;396:193-196.
187. Mount NJ,Abrahart RJ.Load or concentration, logged or unlogged? Addressing ten years of uncertainty in neural network suspended sediment prediction.Hydrological Processes. 2011b;25:3144-3157.
188. Mukerji A,Chatterjee C,Singh Raghuwanshi N.Flood forecasting using ANN, neuro-fuzzy, and neuro-GA models.ASCE Journal of Hydrologic Engineering. 2009;14:647-652.
189. Nabney ITNetlab: Algorithms for Pattern Recognition. New York: Springer; 2002:.
190. National Oceanic and Atmospheric Administration (NOAA) (2011) National Weather Service Middle Atlantic River Forecast Center: The models and the final product. Available at: http://www.erh.noaa.gov/marfc/Science/models.html.
191. Natschläger T,Maass W,Markram H.The 'Liquid Computer': A novel strategy for real-time computing on time series.Special Issue on Foundations of Information Processing of TELEMATIK. 2002;8:39-43.
192. Nayak PC,Sudheer KP,Jain SK.Rainfall-runoff modeling through hybrid intelligent system.Water Resources Research. 2007;43:W07415.
193. Nayak PC,Sudheer KP,Rangan DM,Ramasastri KS.A neuro-fuzzy computing technique for modeling hydrological time series.Journal of Hydrology. 2004;291:52-66.
194. Nayak PC,Sudheer KP,Rangan DM,Ramasastri KS.Short-term flood forecasting with a neurofuzzy model.Water Resources Research. 2005;41:W04004.
195. Nie J,Linkens DA.Fast self-learning multivariable fuzzy controllers constructed from a modified CPN network.International Journal of Control. 1994;60:369-393.
196. Nilsson P,Uvo CB,Berndtsson R.Monthly runoff simulation: Comparing and combining conceptual and neural network models.Journal of Hydrology. 2006;321:344-363.
197. Nourani V,Komasi M,Mano A.A multivariate ANN-wavelet approach for rainfall-runoff modelling.Water Resources Management. 2009;23:2877-2894.
198. Oreskes NModels in Ecosystem Science. Canham CDWCole JLauenroth WK, ed. N: Princeton University Press: Princeton; 2003:13-31.
199. Pan T,Wang R.Using recurrent neural networks to reconstruct rainfall-runoff processes.Hydrological Processes. 2005;19:3603-3619.
200. Parasuraman K,Elshorbagy A.Cluster-based hydrologic prediction using genetic algorithm-trained neural networks.ASCE Journal of Hydrologic Engineering. 2007;12:52-62.
201. Parasuraman K,Elshorbagy A,Carey SK.Spiking modular neural networks: A neural network modeling approach for hydrological processes.Water Resources Research. 2006;42:W05412.
202. Partal T.River flow forecasting using different artificial neural network algorithms and wavelet transform.Canadian Journal of Civil Engineering. 2009;36:26-39.
203. PoliRLangdonWBMcPheeNF (2008) A field guide to genetic programming. Available at: http://www.gp-field-guide.org.uk.
204. PotterMA (1997) The design and analysis of a computational model of cooperative coevolution. PhD thesis, George Mason University, Fairfax, VA.
205. Prada-Sarmiento F,Obregon-Neira N.Forecasting of monthly streamflows based on artificial neural networks.ASCE Journal of Hydrologic Engineering. 2009;14:1390-1395.
206. Pramanik N,Panda RK.Application of neural network and adaptive neuro-fuzzy inference systems for river flow prediction.Hydrological Sciences Journal. 2009;54:247-260.
207. Quinlan JRProceedings 5th Australian Joint Conference on Artificial Intelligence. Adams ASterling L, ed. Singapore: World Scientific; 1992:343-348.
208. Rahnama MB,Noury M.Developing of Halil River rainfall-runoff model, using conjunction of wavelet transform and artificial neural networks.Research Journal of Environmental Sciences. 2008;2:385-292.
209. Raman H,Sunilkumar N.Multivariate modelling of water resources time series using artificial neural networks.Hydrological Sciences Journal. 1995;40:145-163.
210. Reed RD,Marks RJNeural Smithing. Cambridge, MA: The MIT Press; 1999:.
211. Remesan R,Ahmadi A,Shamim MA,Han D.Effect of data time interval of real-time flood forecasting.Journal of Hydroinformatics. 2010;12:396-407.
212. Remesan R,Shamim MA,Han D,Mathew J.Runoff prediction using an integrated hybrid modelling scheme.Journal of Hydrology. 2009;372:48-60.
213. Rowinski PM,Piotrowski A.Estimation of parameters of the transient storage model by means of multi-layer perceptron neural networks.Hydrological Sciences Journal. 2008;53:165-178.
214. Rumelhart DE,Hinton GE,Williams RJParallel Distributed Processing: Explorations in the Microstructures of Cognition. Rumelhart DEMcClelland JL, ed. Cambridge, MA: The MIT Press; 1986:318-362.
215. Samsudin R,Saad P,Shabri A.A hybrid least squares support vector machines and GMDH approach for river flow forecasting.Hydrology and Earth System Sciences Discussions. 2010;7:3691-3731.
216. SarleWS (1995) Stopped training and other remedies for overfitting. In: Proceedings 27th Symposium on the Interface: Computing Science and Statistics, Pittsburgh, 21-24 June, 352-360. Available at: ftp://ftp.sas.com/pub/neural/inter95.ps.Z.
217. Satyaji Rao YR,Krishna BNew Approaches to Hydrological Prediction in Data-sparse Regions. Yilmaz KKYucel IGupta HV, ed. Wallingford: International Association of Hydrological Sciences, Publication; 2009:101-111.
218. Savic D,Khu S-TEncyclopedia of Hydrological Sciences. Anderson MG, ed. New York: Wiley; 2005:331-348.
219. Sedki A,Ouazar D,El Mazoudi E.Evolving neural network using real coded genetic algorithm for daily rainfall-runoff forecasting.Expert Systems with Applications. 2009;36:4523-4527.
220. See LM,Abrahart RJ.Multi-model data fusion for hydrological forecasting.Computers and Geosciences. 2001;27:987-994.
221. See LM,Openshaw S.Applying soft computing approaches to river level forecasting.Hydrological Sciences Journal. 1999;44:763-778.
222. See LM,Openshaw S.A hybrid multi-model approach to river level forecasting.Hydrological Sciences Journal. 2000;45:523-536.
223. See LM,Jain A,Dawson CW,Abrahart RJPractical Hydroinformatics: Computational Intelligence and Technological Developments in Water Applications. Abrahart RJSee LMSolomatine DP, ed. Berlin: Springer; 2008:87-99.
224. Sha W.Comment on 'Flow forecasting for a Hawaii stream using rating curves and neural networks' by G.B. Sahoo and C. Ray [Journal of Hydrology 317 (2006) 63-80].Journal of Hydrology. 2007;340:119-121.
225. Shamseldin AY.Artificial neural network model for river flow forecasting in a developing country.Journal of Hydroinformatics. 2010;12:22-35.
226. Shamseldin AY,O'Connor KM.A non-linear neural network technique for updating of river flow forecasts.Hydrology and Earth Systems Sciences. 2001;5:577-597.
227. Shamseldin AY,Ahmed EN,O'Connor KM.Comparison of different forms of the multi-layer feed-forward neural network method used for river flow forecast combination.Hydrology and Earth System Sciences. 2002;6:671-684.
228. Shamseldin AY,O'Connor KM,Liang GC.Methods for combining the outputs of different rainfall-runoff models.Journal of Hydrology. 1997;197:203-229.
229. Sharkey AJCCombining Artificial Neural Networks: Ensemble and Modular Multi-Net Systems. London: Springer; 1999:.
230. Sharma A.Seasonal to interannual rainfall probabilistic forecasts for improved water supply management: Part 1 - a strategy for system predictor identification.Journal of Hydrology. 2000;239:232-239.
231. Sharma SK,Tiwari KN.Bootstrap based artificial neural network (BANN) analysis for hierarchical prediction of monthly runoff in Upper Damodar Valley Catchment.Journal of Hydrology. 2009;374:209-222.
232. Shim KC,Fontane D,Labadie JW.Spatial decision support system for integrated river basin flood control.Journal of Water Resources Planning and Management. 2002;128:190-201.
233. Shrestha DL,Kayastha N,Solomatine DP.A novel approach to parameter uncertainty analysis of hydrological models using neural networks.Hydrology and Earth System Sciences. 2009;13:1235-1248.
234. Shrestha RR,Theobald S,Nestmann F.Simulation of flood flow in a river system using artificial neural networks.Hydrology and Earth System Sciences. 2005;9:313-321.
235. Shu C,Burn D.Artificial neural network ensembles and their application in pooled flood frequency analysis.Water Resources Research. 2004;40:W09301.
236. Singh P,Deo MC.Suitability of different neural networks in daily flow forecasting.Applied Soft Computing. 2007;7:968-978.
237. Sivakumar B,Jayawardena AW,Fernando TMGH.River flow forecasting: Use of phase-space reconstruction and artificial neural networks approaches.Journal of Hydrology. 2002;265:225-245.
238. Smith J,Eli RNWater Resources Planning and Management: Saving a Threatened Resource - In Search of Solutions. Proceedings of the Water Resources Sessions at Water Forum '92. Karamouz M, ed. New York: ASCE; 1992:56-61.
239. Smith J,Eli RN.Neural network models of rainfall-runoff process.Journal of Water Resources Planning Management. 1995;121:499-508.
240. Smith MB,Seo D-J,Koren VI, et al.The distributed model intercomparison project (DMIP): Motivation and experiment design.Journal of Hydrology. 2004;298:4-26.
241. Solomatine DPProceedings of the iEMSs 3rd Biennial Meeting: 'Summit on Environmental Modelling and Software'. Voinov AJakeman ARizzoli A, ed. Burlington, USA. Manno, Switzerland: International Environmental Modelling and Software Society; 2006:.
242. Solomatine DP,Avila Torres LAHydroinformatics'96: Proceedings 2nd International Conference on Hydroinformatics. Müller A, ed. Rotterdam: Zürich, Switzerland A.A. Balkema.; 1996:201-206.
243. Solomatine DP,Shrestha DL.A novel method to estimate model uncertainty using machine learning techniques.Water Resources Research. 2009;45:W00B11.
244. Solomatine DP,Xue Y.M5 model trees and neural networks: Application to flood forecasting in the upper reach of the Huai River in China.ASCE Journal of Hydrologic Engineering. 2004;9:491-501.
245. Solomatine DP,See LM,Abrahart RJPractical Hydroinformatics: Computational Intelligence and Technological Developments in Water Application. Abrahart RJSee LMSolomatine DP, ed. Berlin: Springer; 2008:17-30.
246. Srinivasulu S,Jain A.River flow prediction using an integrated approach.Journal of Hydrologic Engineering. 2009;14:75-83.
247. Srivastav RK,Sudheer KP,Chaubey I.A simplified approach to quantifying predictive and parametric uncertainty in artificial neural network hydrologic models.Water Resources Research. 2007;43:W10407.
248. Stefansson A,Koncar N,Jones AJ.A note on the gamma test.Neural Computing Applications. 1997;5:131-133.
249. Sudheer KP.Knowledge extraction from trained neural network river flow models.ASCE Journal of Hydrologic Engineering. 2005;10:264-269.
250. Sudheer KP,Jain A.Explaining the internal behaviour of artificial neural network river flow models.Hydrological Processes. 2004;18:833-844.
251. Talei A,Chua LHC,Quek C.A novel application of a neuro-fuzzy computational technique in event-based rainfall-runoff modeling.Expert Systems with Applications. 2010;37:7456-7468.
252. Thirumalaiah K,Deo MC.Hydrological forecasting using neural networks.ASCE Journal of Hydrologic Engineering. 2000;5:180-189.
253. Tiwari MK,Chatterjee C.Uncertainty assessment and ensemble flood forecasting using bootstrap based artificial neural networks (BANNs).Journal of Hydrology. 2010;382:20-33.
254. Tokar AS,Johnson PA.Rainfall-runoff modeling using artificial neural networks.ASCE Journal of Hydrologic Engineering. 1999;4 (3): 232-239.
255. Toth EPractical Hydroinformatics: Computational Intelligence and Technological Developments in Water Applications. Abrahart RJSee LMSolomatine DP, ed. Berlin: Springer, Water Science and Technology Library; 2008:113-125.
256. Toth E.Classification of hydro-meteorological conditions and multiple artificial neural networks for streamflow forecasting.Hydrology and Earth System Sciences. 2009;13:1555-1566.
257. Toth E,Brath AHydrological Risk, Recent Advances in Peak River Flow Modelling, Prediction and Real-Time Forecasting - Assessment of the Impacts of Land-Use and Climate Changes. Brath AMontanari AToth E, ed. Cosenza: Ed. Bios; 2004:247-261.
258. Toth E,Brath A.Multistep ahead streamflow forecasting: Role of calibration data in conceptual and neural network modelling.Water Resources Research. 2007;43:W11405.
259. Vrugt JA,Gupta HV,Bastidas LA,Bouten W.Effective and efficient algorithm for multiobjective optimization of hydrologic models.Water Resources Research. 2003;39:1214.
260. Wang W,Ding J.Wavelet network model and its application to the prediction of hydrology.Nature and Science. 2003;1:67-71.
261. Wang W,Jin J,Li Y.Prediction of inflow at three gorges dam in Yangtze River with wavelet network model.Water Resources Management. 2009;23:2791-2803.
262. Wang Y,Witten IHProceedings of Poster Papers 9th European Conference on Machine Learning. van Someren MWidmer G, ed. Prague: University of Economics, Faculty of Informatics and Statistics; 1997:128-137.
263. Weigend AS,Huberman BA,Rumelhart DENonlinear Modeling and Forecasting. Casdagli MEubank S, ed. Redwood City, CA: Addison-Wesley; 1992:395-432.
264. Wilby RL,Abrahart RJ,Dawson CW.Detection of conceptual model rainfall-runoff processes inside an artificial neural network.Hydrological Sciences Journal. 2003;48:163-181.
265. Intercomparison of Conceptual Models used in Operational Hydrological Forecasting. Geneva: WMO; 1975:.
266. Intercomparison of Models of Snowmelt Runoff. Geneva: WMO; 1986:.
267. Simulated Real-Time Intercomparison of Hydrological Models. Geneva: WMO; 1992:.
268. Wu CL,Chau KW.A flood forecasting neural network model with genetic algorithm.International Journal of Environment and Pollution. 2006;28:261-273.
269. Wu CL,Chau KW,Li YS.Methods to improve neural network performance in daily flows prediction.Journal of Hydrology. 2009;372:80-93.
270. Xiong L,O'Connor KM.Comparison of four updating models for real-time river flow forecasting.Hydrological Sciences Journal. 2002;47:621-639.
271. Xiong L,Shamseldin AY,O'Connor KM.A non-linear combination of the forecasts of rainfall-runoff models by the first-order Takagi-Sugeno fuzzy system.Journal of Hydrology. 2001;245:196-217.
272. Yang C-C,Chen C-S.Application of integrated back-propagation network and self organizing map for flood forecasting.Hydrological Processes. 2009;23:1313-1323.
273. Yang K,Meho LI.Citation analysis: A comparison of Google Scholar, Scopus, and Web of Science.Proceedings of the American Society for Information Science and Technology. 2007;43:1-15.
274. Yonaba H,Anctil F,Fortin V.Comparing sigmoid transfer functions for neural network multistep ahead streamflow forecasting.ASCE Journal of Hydrologic Engineering. 2010;15:275-283.
275. Zhang B,Govindaraju RS.Prediction of watershed runoff using Bayesian concepts and modular neural networks.Water Resources Research. 2000;36:753-762.
276. Zhang X,Liang F,Srinivasan R,Van Liew M.Estimating uncertainty of streamflow simulation using Bayesian neural networks.Water Resources Research. 2009;45:W02403.
277. Zhao R,Chen S.A hybrid fuzzy and neural network model for hydrological forecasting in ungauged basins.IAHS-AISH Publication. 2008;319:39-48.
278. Zou R,Lung W-S,Wu J.An adaptive neural network embedded genetic algorithm approach for inverse water quality modeling.Water Resources Research. 2007;43:W08427.
279. Zounemat-Kermani M,Teshnehlab M.Using adaptive neuro-fuzzy inference system for hydrological time series prediction.Applied Soft Computing. 2008;8:928-936.