Abstract
The purpose of this paper is to study the potential uses of Artificial Neural Networks (ANNs) in estimating monthly precipitation for the City of Memphis, Tennessee. One of the popular models, the backpropagation algorithm (BP), was developed and installed in an internal program. Annual precipitation data from the City of Memphis provides the basis data to train the networks. The trained networks were then used to verify and predict precipitation for the City of Memphis. The results indicated that ANNs can recognize the precipitation pattern and provide an analogous precipitation trend to the existing precipitation data. One of the major advantages of this method is that its performance does not require a large quantity of data in the trend analysis.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Djukanovic, M., and Babic, B. (1991) Unsupervised/supervised learning concept for 24-hour lead forecasting, Proceedings of ANNIE (Artificial Neural Networks in Engineering), 819–827.
Hassoun, M., and Spitzer, R. (1988) Neural network identification and extraction of repetitive super-imposed pulses in noisy 1-D signals, Neural Networks Supplement 1, 1443.
Khotanzad, A., and Fowler M. (1991) Neural network based time series forecasting, Proceedings of ANNIE (Artificial Neural Networks in Engineering), 813–818.
Malasri, S., and Lin L. Y.(1992) Forecasting with artificial neural networks, Proceeding of Arkansas Academy of Science, 1–7.
Malasri, S., Madhavan, K., and Lin L. Y.(1991) Soil classifier: expert systems + neural nets, Proceedings of ANNIE (Artificial Neural Networks in Engineering), 807–812.
Rumelhart, D.E., Hinton G.E., and Williams R. J. (1986) Learning internal representations by error propagation, MIT Press, Cambridge, Mass., USA, 1, 318–362.
Werbos, P.J. (1974) Beyond regression: new tools for prediction and analysis in the behavioral sciences, Unpublished Ph.D. Dissertation, Harvard University, Cambridge, Mass., USA.
Williams, T.P., Khajuria, A., and Balaguru P. (1992) Neural network for predicting Concrete Strength, Proceedings of the 8th Conference on Computing in Civil Engineering, 1082–1088.
Windrow, B., and Winter R. (1988) Neural nets for adaptive filtering and adaptive pattern recognition, Proceeding of IEEE Computer, 25–29.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1996 Springer Science+Business Media Dordrecht
About this chapter
Cite this chapter
Kung, HT., Lin, L.Y., Malasri, S. (1996). Use of Artificial Neural Networks in Precipitation Forecasting. In: Jones, J.A.A., Liu, C., Woo, MK., Kung, HT. (eds) Regional Hydrological Response to Climate Change. The GeoJournal Library, vol 38. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-5676-9_9
Download citation
DOI: https://doi.org/10.1007/978-94-011-5676-9_9
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-010-6394-4
Online ISBN: 978-94-011-5676-9
eBook Packages: Springer Book Archive