A self-organizing radial basis network for estimating riverine fish diversity

Journal of Hydrology

Volumn 476, Issue , 2013, Pages 280-289

  Chang, Fi John ^a   Tsai, Wen Ping ^a   Chen, Hung Kwai ^b   Yam, Rita Sau Wai ^a   Herricks, Edwin E ^c  

^a NATIONAL TAIWAN UNIVERSITY   (Taiwan)

^b MINISTRY OF ECONOMIC AFFAIRS   (Taiwan)

^c United States of America   (United States)

Author keywords

Fish communities; Moving average; River ecosystems; Self organizing radial basis network (SORBN); Taiwan Eco hydrologic Indicator System (TEIS)

Indexed keywords

FISH COMMUNITIES; INDICATOR SYSTEM; MOVING AVERAGES; RADIAL BASIS NETWORKS; RIVER ECOSYSTEM;

CONFORMAL MAPPING; ECOSYSTEMS; ESTIMATION; FISH; RADIAL BASIS FUNCTION NETWORKS; SELF ORGANIZING MAPS; STREAM FLOW;

DATA FLOW ANALYSIS;

AQUATIC ECOSYSTEM; ASSESSMENT METHOD; DATA SET; ECOHYDROLOGY; ENVIRONMENTAL CONDITIONS; ESTIMATION METHOD; FISH; HABITAT TYPE; RELIABILITY ANALYSIS; SPECIES DIVERSITY; SUSTAINABILITY;

TAIWAN;

EID: 84871004558     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhydrol.2012.10.038     Document Type: Article

References (34)

1. Arthington A.H., Bunn S.E., Poff N.L., Naiman R.J. The challenge of providing environmental flow rules to sustain river ecosystems. Ecol. Appl. 2006, 16(4):1311-1318.
2. Bragg O.M., et al. Approaching the physical-biological interface in rivers: a review of methods for ecological evaluation of flow regimes. Prog. Phys. Geogr. 2005, 29(4):506.
3. Brown L.R., Ford T. Effects of flow on the fish communities of a regulated California river: implications for managing native fishes. River Res. Appl. 2002, 18(4):331-342.
4. Bunn S.E., Arthington A.H. Basic principles and ecological consequences of altered flow regimes for aquatic biodiversity. Environ. Manage. 2002, 30(4):492-507.
5. Chang F.J., Chang Y.T. Adaptive neuro-fuzzy inference system for prediction of water level in reservoir. Adv. Water Resour. 2006, 29:1-10.
6. Chang F.J., Tsai M.J., Tsai W.P., Herricks E.E. Assessing the ecological hydrology of natural flow conditions in Taiwan. J. Hydrol. 2008, 354(1-4):75-89.
7. Chang F.J., Chang L.C., Kao H.S., Wu G.R. Assessing the effort of meteorological variables for evaporation estimation by Self-Organizing Map Neural Network. J. Hydrol. 2010, 384:118-129.
8. Chang F.J., Chang L.C., Wang Y.S. Enforced self-organizing map neural networks for river flood forecasting. Hydrol. Process. 2007, 21:741-749.
9. Chang, F.J., Herricks, E.E., 2005. Integration of Ecohydrology in Managing Water Resources. Water Resources Planning Institute Technical Report, Water Resources Agency of MOEA, Taiwan. ISBN: 986-00-3534-3.
10. Chang F.J., Tsai W.P., Wu T.C., Chen H.K., Herricks E.E. Identifying natural flow regimes using fish communities. J. Hydrol. 2011, 409:328-336.
11. Chang F.J., Wu T.C., Tsai W.P., Herricks E.E. Defining the ecological hydrology of Taiwan Rivers using multivariate statistical methods. J. Hydrol. 2009, 376:235-242.
12. Chang L.C., Chang F.J., Wang Y.P. Auto-configuring RBF networks for chaotic time series and flood forecasting. Hydrol. Process. 2009, 23:2450-2459.
13. Chang Y.T., Chang L.C., Chang F.J. Intelligent control for modeling of real-time reservoir operation, part II: artificial neural network with operating rule curves. Hydrol. Process. 2005, 19(7):1431-1444.
14. Chen Y.H., Chang F.J. Evolutionary artificial neural networks for hydrological systems forecasting. J. Hydrol. 2009, 367:125-137.
15. Chiang Y.M., Hsu K.L., Chang F.J., Hong Y., Sorooshian S. Merging multiple precipitation sources for flash flood forecasting. J. Hydrol. 2007, 340(3-4):183-196.
16. Gholizadeh S., Salajegheh E. Optimal design of structures for earthquake loading by self organizing radial basis function neural networks. Adv. Struct. Eng. 2010, 13(2):339-356.
17. Gordon N.D., McMahon T.A., Finlayson B.L., Gippel C.J., Nathan R.J. Stream Hydrology - An Introduction for Ecologists 2004, John Wiley and Sons, New York.
18. Hughes D.A., Hannart P. A desktop model used to provide an initial estimate of the ecological instream flow requirements of rivers in South Africa. J. Hydrol. 2003, 270:167-181.
19. Karr J.R. Assessment of biotic integrity using fish communities. Fisheries 1981, 6:21-27.
20. Kohonen T. Self-organized formation of topologically correct feature maps. Biol. Cybern. 1982, 43:59-69.
21. Moradkhani H., Hsu K., Gupta H.V., Sorooshian S. Improved streamflow forecasting using self-organizing radial basis function artificial neural network. J. Hydrol. 2004, 295(1-4):246-262.
22. Olden J.D., Poff N.L. Redundancy and the choice of hydrologic indices for characterizing streamflow regimes. River Res. Appl. 2003, 19(2):101-121.
23. Pinthong P., Gupta A.D., Babel M.S., Weesakul S. Improved reservoir operation using hybrid genetic algorithm and neurofuzzy computing. Water Resour. Manage. 2009, 23(4):697-720.
24. Rand W.M. Objective criteria for the evaluation of clustering methods. J. Am. Stat. Assoc. (Am. Stat. Assoc.) 1971, 66(336):846-850.
25. Shannon C.E. A mathematical theory of communication. Bell Syst. Tech. J. 1948, 27(379-423):623-656.
26. Sudheer K.P., Chaubey I., Garg V. Lake water quality assessment from Landsat thematic mapper data using neural network: an approach to optimal band combination selection. J. Am. Water Resour. Assoc. 2006, 42(6):1683-1695.
27. Suen, J.P., 2005. Ecologically based Methods for Multi-Objective Water Resources Management in Taiwan. PhD Dissertation, University of Illinois, Urbana, USA.
28. Suen J.P., Eheart J.W. Reservoir management to balance ecosystem and human needs: incorporating the paradigm of the ecological flow regime. Water Resour. Res. 2006, 42(3):W03417. 10.1029/2005WR004314.
29. Suen J.P., Herricks E.E. Investigating the causes of fish community change in the Tahan River (Taiwan) using an autecology matrix. Hydrobiologia 2006, 568(1):317-330.
30. Suen J.P., Herricks E.E. Developing fish community based ecohydrological indicators for water resources management in Taiwan. Hydrobiologia 2009, 625:223-234.
31. Toth E. Classification of hydro-meteorological conditions and multiple artificial neural networks for streamflow forecasting. Hydrol. Earth Syst. Sci. 2009, 13(9):1555-1566.
32. Yeung K.Y., Ruzzo W.L. Principal component analysis for clustering gene expression data. Bioinformatics 2001, 17(9):763-774.
33. Yilmaz I., Erik N.Y., Kaynar O. Different types of learning algorithms of artificial neural network (ANN) models for prediction of gross calorific value (GCV) of coals. Sci. Res. Essays 2010, 5(16):2242-2249.
34. Yuan, J., Shu, J., 2008. Application of neural network model to prediction of water quality of the Yellow River. J. Ecol. Rural Environ. 24(2), 49-51, 60.