Abstract
Artificial Neural Network (ANN) models are highly flexible function approximators, which have shown their utility in a broad range of ecological modelling applications. The rapid emergence of ANN applications in the field of ecological modelling can be attributed to their advantages over standard statistical approaches. Such flexibility provides a powerful tool for forecasting and prediction, however, the large number of parameters that must be selected only serves to complicate the design process. In most practical circumstances, the design of an ANN is heavily based on heuristic trial-and-error processes with only broad rules of thumb to guide along the way.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
ASCE Task Committee on Application of Artificial Neural Networks in Hydrology (2000) Artificial neural networks in hydrology. II
BioComp Systems, I. (1998) NeuroGenetic Optimizer (NGO). Redmond, WA. Hydrologic 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. Mathematics of Control Signals and Systems, 2: 203–314.
Dandy GC, Simpson AR, Murphy LJ (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 of Hydrologic 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. Biological 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 Statistical 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. Govindaraju and A. R. Rao (Eds), Artificial Neural Networks in Hydrology, Kluwer Academic Publishers pp. 348.
Maier HR, Dandy GC (2000b) Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environmental Modelling and Software, 15: 101–124.
Maier HR, Dandy GC, Burch MD(1998) Use of artificial neural networks for modelling cyanobacteria Anabaena spp. in the River Murray, South Australia. Ecological Modelling, 105: 257–272.
Maier HR, Sayed T, Lence BJ(2000) Forecasting cyanobacterium Anabaena spp. using Bspline neurofuzzy models, 2nd International Conference on Applications of Machine Learning to Ecological Modelling, Adelaide, Australia.
Masters T (1993) Practical Neural Network Recipes in C++, Academic Press, San Diego, 493 pp.
Masters T (1995) Neural, Novel and Hybrid Algorithms for Time Series Prediction, John Wiley and Sons, New York, 514 pp.
NeuralWare (1998) Neural Computing: A Technology Handbook for NeuralWorks Professional II/PLUS and NeuralWorks Explorer, Aspen Technology Inc., USA, 324 pp.
Simpson AR, Dandy GC, Murphy LJ (1994) Genetic algorithms compared to other techniques for pipeline optimisation. Journal of Water Resources Planning and Management, ASCE, 120(4): 423–443.
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Bowden, G.J., Dandy, G.C., Maier, H.R. (2003). An Evaluation of Methods for the Selection of Inputs for an Artificial Neural Network Based River Model. In: Recknagel, F. (eds) Ecological Informatics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-05150-4_11
Download citation
DOI: https://doi.org/10.1007/978-3-662-05150-4_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-05152-8
Online ISBN: 978-3-662-05150-4
eBook Packages: Springer Book Archive