Chapter 11· Input Selection for an Aigal Bloom Model
231
References
ASCE Task Committee on Application of Artificial Neural Networks in Hydrology (2000)
Artificial neural networks in hydrology. 11
BioComp Systems, I. (1998) NeuroGenetic Optimizer (NGO). Redrnond, W A. Hydrologie
applications. Journal of Hydrologic Engineering, ASCE, 5(2): 124-137.
Bowden GJ, Maier HR, Dandy GC (2000) Optimal division of data for neural network
models in water resources applications. submitted to Water Resources Research.
Cai S, Toral H, Qiu J, Archer JS (1994) Neural network based objective flow regime
identification in air-water two phase flow. The Canadian Journal of Chemical
Engineering, 72: 440-445.
Chakraborty K, Mehrotra K, Mohan CK, Ranka S (1992) Forecasting the behavior of
multivariate time series using neural networks. Neural Networks, 5: 961-970.
Chon TS, Park YS, Moon KH, Cha EY (1996) Patternizing communities by using an
artificial neural network. Ecological Modelling, 90: 69-78.
Cybenko G (1989) Approximation by superpositions of a sigmoidal function. Mathematies
of Control Signals and Systems, 2: 203-314.
Dandy GC, Simpson AR, Murphy LI (1996) An improved genetic algorithm for pipe
network optimisation. Water Resources Research, 32(2): 449-458.
Downing K (1998) Using evolutionary computation techniques in environmental
modelling. Environmental Modelling & Software, 13(5-6): 519-528.
Fernando DAK, Jayawardena AW (1998) Runoff forecasting using RBF networks with
OLS algorithm. Journal ofHydrologic Engineering, 3(3): 203-209.
Foody GM (1999) Applications of the self-organising feature map neural network in
community data analysis. Ecological Modelling, 120: 97-107.
Goldberg DE (1989) Genetic Algorithms in Search, Optimization and Machine Learning,
Addison-Wesley, Reading, MA, 412 pp.
Howard LM, D'Angelo DJ (1995) GA-P: a genetic algorithm and genetic programming
hybrid. IEEE Expert, 10(3): 11-15.
Islam S, Kothari R (2000) Artificial neural networks in remote sensing of hydrologic
processes. Journal of Hydrologic Engineering, 5(2): 138-144.
Jolliffe IT (1986) Principal Component Analysis, Springer-Verlag New York Inc., New
York, 271 pp.
Kaski S, Kangas J, Kohonen T (1998) Bibliography of Self-Organizing Map (SOM)
Papers: 1981-1997. Neural Computing Surveys, 1: 102-350.
Kohonen T (1982) Self-organized formation of topologically correct feature maps.
Biologieal Cybernetics, 43: 59-69.
Kohonen T (1990) The Self-Organizing Map. Proc. IEEE, 78(9): 1464-1480.
Lachtermacher G, Fuller JD (1994) Backpropagation in hydrological time series
forecasting. In K. W. Hipel, A. I. McLeod, U. S. Panu, and V. P. Singh (Eds),
Stochastic and Statistieal Methods in Hydrology and Environmental Engineering,
Kluwer Academic Publishers, Dordrecht, pp. 229-242.
Maier HR, Dandy GC (1997) Determining inputs for neural network models of multivariate
time series. Microcomputers in Civil Engineering, 12(5): 353-368.
Maier HR, Dandy GC (2000a) Application of Artificial Neural Networks to Forecasting of
Surface Water Quality Variables: Issues Applications and Challenges. In R. S.
231
References
ASCE Task Committee on Application of Artificial Neural Networks in Hydrology (2000)
Artificial neural networks in hydrology. 11
BioComp Systems, I. (1998) NeuroGenetic Optimizer (NGO). Redrnond, W A. Hydrologie
applications. Journal of Hydrologic Engineering, ASCE, 5(2): 124-137.
Bowden GJ, Maier HR, Dandy GC (2000) Optimal division of data for neural network
models in water resources applications. submitted to Water Resources Research.
Cai S, Toral H, Qiu J, Archer JS (1994) Neural network based objective flow regime
identification in air-water two phase flow. The Canadian Journal of Chemical
Engineering, 72: 440-445.
Chakraborty K, Mehrotra K, Mohan CK, Ranka S (1992) Forecasting the behavior of
multivariate time series using neural networks. Neural Networks, 5: 961-970.
Chon TS, Park YS, Moon KH, Cha EY (1996) Patternizing communities by using an
artificial neural network. Ecological Modelling, 90: 69-78.
Cybenko G (1989) Approximation by superpositions of a sigmoidal function. Mathematies
of Control Signals and Systems, 2: 203-314.
Dandy GC, Simpson AR, Murphy LI (1996) An improved genetic algorithm for pipe
network optimisation. Water Resources Research, 32(2): 449-458.
Downing K (1998) Using evolutionary computation techniques in environmental
modelling. Environmental Modelling & Software, 13(5-6): 519-528.
Fernando DAK, Jayawardena AW (1998) Runoff forecasting using RBF networks with
OLS algorithm. Journal ofHydrologic Engineering, 3(3): 203-209.
Foody GM (1999) Applications of the self-organising feature map neural network in
community data analysis. Ecological Modelling, 120: 97-107.
Goldberg DE (1989) Genetic Algorithms in Search, Optimization and Machine Learning,
Addison-Wesley, Reading, MA, 412 pp.
Howard LM, D'Angelo DJ (1995) GA-P: a genetic algorithm and genetic programming
hybrid. IEEE Expert, 10(3): 11-15.
Islam S, Kothari R (2000) Artificial neural networks in remote sensing of hydrologic
processes. Journal of Hydrologic Engineering, 5(2): 138-144.
Jolliffe IT (1986) Principal Component Analysis, Springer-Verlag New York Inc., New
York, 271 pp.
Kaski S, Kangas J, Kohonen T (1998) Bibliography of Self-Organizing Map (SOM)
Papers: 1981-1997. Neural Computing Surveys, 1: 102-350.
Kohonen T (1982) Self-organized formation of topologically correct feature maps.
Biologieal Cybernetics, 43: 59-69.
Kohonen T (1990) The Self-Organizing Map. Proc. IEEE, 78(9): 1464-1480.
Lachtermacher G, Fuller JD (1994) Backpropagation in hydrological time series
forecasting. In K. W. Hipel, A. I. McLeod, U. S. Panu, and V. P. Singh (Eds),
Stochastic and Statistieal Methods in Hydrology and Environmental Engineering,
Kluwer Academic Publishers, Dordrecht, pp. 229-242.
Maier HR, Dandy GC (1997) Determining inputs for neural network models of multivariate
time series. Microcomputers in Civil Engineering, 12(5): 353-368.
Maier HR, Dandy GC (2000a) Application of Artificial Neural Networks to Forecasting of
Surface Water Quality Variables: Issues Applications and Challenges. In R. S.