Abstract
As has been seen in previous chapters, artificial neural networks (ANNs) are powerful and useful tools in the field of hydrology. Presented here is an application of ANNs to long range precipitation prediction in California using large-scale climatological parameters. In addition, a determination will be made about extracting information from the trained network in order to learn how the ANN’s “thinking” produces the prediction.
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References
Barnston, A., and Livezey, E.J. (1987) Classification, seasonality, and persistence of low-frequency atmospheric circulation patterns. Monthly Weather Review, 115, 1082–1126.
Barnston, A., and Livezey, E.J. (1991) Statistical prediction ofJanuary February mean Northern Hemisphere lower- tropospheric climate from the 11-year solar cycle and the southern oscillation for west and east QBO phases. Journal of Climate, 4:2, 249–262.
Fausett, L. (1994): Fundamentals of Neural Networks Prentice-Hall, Inc., 461pp
Hashem, S. (1992) Sensitivity Analysis for Feedforward Artificial Neural Networks with Differentiable Activation Functions, IEEE
Hinton, G. E., 1992: How neural networks learn from experience. Scientific American, September 144–151.
Ishikawa, M. (1994) Structutral Learning and its Application to Rule Extraction, IEEE Proceedings,
Kahya, E., and J. A. Dracup, 1993: U.S. streamflow patterns in relation to the El Niño/Southern Oscillation. Water Resources Research, 29:8, 2491–2503.
Kane, R. and Milgram, M. (1994) Financial Forecasting and Rules Extraction from Trained Networks, IEEE Proceedings,
Kuligowski, R.J., and A.P. Barrows, 1998: Experiments in short-term precipitation forecasting using artificial neural networks. Monthly Weather Review, 126:2, 40–48
Lau, N-C., I. M. Held, and J. D. Neelin, 1988: The Madden-Julian Oscillation in an idealized general circulation model. Journal of the Atmospheric Sciences, 45:24, 3810–3832.
Masters, T., 1993: Practical Neural Network Recipes in C++, Academic Press, 493pp.
Mo, K.C., and R.E. Livezey, 1986: Tropical-extratropical geopotential height teleconnections during the Northern Hemisphere winter. Monthly Weather Review, 114:12, 2488–2515.
NDMC/National Drought Mitigation Center, cited January 15, 1998, Predicting drought. [Available online, from http://enso.unl.edu.ndmc/enigma/predict.htm] .
NOAA/National Oceanic and Atmospheric Administration, Climate Analysis Center, cited January 15,1998, CPC-Data Current monthly atmospheric and SST index values. [Available online from http://www.nic.fb4.noaa.gov/data/cddb] .
NOAA/National Oceanic and Atmospheric Administration, cited January 15, 1998, Teleconnection Pattern Calculations. [Available online from http://nic.fb4.noaa.gov/data/teledoc/telepatcalc.html].
Redmond, K. T., and R W. Koch, 1991: Surface climate and streamflow variability in the western United States and their relationship to large-scale circulation indices. Water Resources Research, 27:9, 2381–2399.
Ropelewski, C.F., and M.S. Halpert, 1989: Precipitation patterns associated with the high index phase of the Southern Oscillation. Journal of Climate, 2, 268–284.
Ropelewski, C.F., and M.S. Halpert, 1987: Global and regional scale precipitation patterns associated with the El Niño/Southern Oscillation. Monthly Weather Review, 115,1606–1626.
Ropelewski, C.F., and M.S. Halpert, 1986: North American precipitation and temperature patterns associated with the El Niño/Southern Oscillation (ENSO). Monthly Weather Review, 114, 2352–2362.
Ropelewski, C.F., and P.D. Jones, 1987: An extension of the Tahiti-Darwin Southern Oscillation index. Monthly Weather Review, 115, 2161–2165.
Silverman, D. and Dracup, J. (in press) Artificial Neural Networks and Long Range Precipitation Prediction in California, Journal of Applied Meteorology
Towell, G. and Shavlik, J. (1993) Extracting Refined Rules from Knowledge-Based Neural Networks, Machine Learning, 13, 71–101
Wallace, J.M., and D. S. Gutzier, 1981: Teleconnections in the potential height field during the Northern Hemisphere winter, Monthly Weather Review, 109, 784–812.
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Silverman, D., Dracup, J.A. (2000). Long Range Precipitation Prediction in California: A Look Inside the “Black Box” of a Trained Network. In: Govindaraju, R.S., Rao, A.R. (eds) Artificial Neural Networks in Hydrology. Water Science and Technology Library, vol 36. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-9341-0_16
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DOI: https://doi.org/10.1007/978-94-015-9341-0_16
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