Abstract
Ice condition forecasts are very important for preventing ice disasters. Because of the complexity of ice conditions, traditional methods could hardly give accurate prediction in the ice condition forecast, especially for the meandering rivers as the Yellow River, while the artificial neural networks (ANNs) have obvious advantage over other traditional methods for forecasting ice condition. An ANN model based on feed-forward back-propagation (FFBP) and improved by Levenberg-Marquardt algorithm is applied to forecast the ice condition. The study is applied to forecasting ice condition of the Yellow River in the Inner Mongolia Region. The forecast results in the winter of 2004–2005 are in good agreement with the measured ones. Simulation also shows that the ANN model is superior to the MLR model and GM (0,1) model.
The work is support by the National Nature Science Foundation of China (SN: 50609031).
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References
ASCE Task Committee. (2000a). Artificial neural networks in Hydrology I. Journal of Hydrologic Engineering, 5(2), 115–123.
ASCE Task Committee. (2000b). Artificial neural networks in Hydrology II. Journal of Hydrologic Engineering, ASCE. 5(2), 124–132.
Chen S.Y., and Ji H. L. (2004). Fuzzy optimization neural network BP approach for ice forecast. Journal of Hydraulic Engineer, 36(6), 114–118.
Cheng C. T., Chau K. W., Sun Y. G., and Lin J. Y. (2005). Long-term prediction of discharges in Manwan Reservoir artificial neural network models. Lecture Notes in Computer Science, 3498, 1040–1045.
Cigizoglu, H.K. (2004). Estimation and forecasting of daily suspended sediment data by multi layer perceptions. Advances in Water Research, 27, 185–195.
Cigizoglu H.K., and Alp M. (2006). Generalized regression neural network in modelling river sediment yield. Advances in Engineering Software, 37, 63–68.
Cigizoglu H.K., and Kisi O. (2006). Methods to improve the neural network performance in suspended sediment estimation. Journal of Hydrology, 317(3–4), 221–238.
Dawson C.W., Abrahart R.J., Shamseldin A.Y., and Wilby R.L. (2006). Flood estimation at ungauged sites using artificial neural networks. Journal of Hydrology, 319(1–4), 391–409.
Eberhart R.C., and Dobbins R.W. (1990). Neural network PC tools: a practical guide. Academic Press, San Diego, 414.
Foltyn E. P., and Shen H. D. (1986). St. Lawrence river freeze-Up forecast. Journal of Waterway, Port, Coastal and Ocean Engineering, 112(4), 467–481.
Ke S. J., Wang M., and others (2002). Yellow river ice conditions. Yellow River Water Resources Publishing House, Zhengzhou.
Maier H. R., and Dandy G. C. (2000). Neural network for the prediction and forecasting of water resources variable: a review of modeling issues and applications. Environmental Modeling and Software, 15, 101–124.
Massie D. D., White K. D., and Daly S. F. (2001). Proceed ings of ice jam with neural networks. Proceeding of the 11th Workshop on River Ice. Canadian Geophysical Union, Ottawa, 2001, 209–216.
Muttil N., and Lee J. H. W. (2005). Genetic programming for analysis and real-time prediction of coastal algal bloom. Ecologiacal Modelling, 189, 363–376.
Muttil N., Chau K. W. (2006). Neural network and genetic programming for modeling a coastal algal bloom. International Journal of Environment and Pollution, 28(3–4), 223–238.
Olden J. D., Poff N.L., and Bledsoe B. P (2006). Incorporating ecological knowledge into ecoinformatics: An example of modeling hierarchically structured aquatic communities with neural networks. Ecological Informatics, 1(1), 33–42.
ahoo G. B., Ray C., and De Carlo E. H. (2006). Use of neural network to predict flash flood and attendant water qualities of a mountainous stream on Oahu, Hawaii. Journal of Hydrology, 327(3–4), 525–538.
Shen H.T., and Yapa P. D. (1985). A unified degree-day method for river ice cover thickness simulation. Canadian Journal of Civil Engineering, 12(3), 54–62.
Shen H.T.(1990). Dynamic transport of river ice. Journal of Hydraulic Research. 28(6), 659–671.
Tsai C. P., Lin C., and Shen J. N. (2002). Neural network for wave forecasting among multi-stations. Ocean Engineering, 29, 1683–1695
Tan C. O., and Beklioglu M. (2006). Modeling complex nonlinear responses of shallow lakes to fish and hydrology using artificial neural networks. Ecological Modelling, 196(1–2), 183–194.
The Ministry of Water Resources of the People’s Republic of China (2000). Hydrographic forecast standard SL250-2000. China WaterPower Press, Beijing.
Van Gent M. R. A., van den Boogaard H. F. P., Pozueta B., and Medin J. R. (2007). Neural network modelling of wave overtopping at coastal structures. Coastal Engineering, 54, 586–593.
Yang K. L., Liu Z. P., and Li G. F. (2001). Simulation of ice jams. Water Resources and Hydropower Engineering, 33(10), 40–47.
Yang K. L, Huo S. Q., Rao S. Q., and Wang T. et al (2006). Ice forecacast system for Ning-Meng Reach of Yellow River. China Institution of Water Resource and Hydropower Research (IWHR) and Hydrology Bureau of Yellow River Conservancy Commission (HBYRCC), Beijing and Zhengzhou, China, 49–52.
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© 2009 Tsinghua University Press, Beijing and Springer-Verlag GmbH Berlin Heidelberg
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Wang, T., Yang, K., Guo, Y. (2009). River Ice Conditions Forecast by Artificial Neural Networks. In: Advances in Water Resources and Hydraulic Engineering. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89465-0_329
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DOI: https://doi.org/10.1007/978-3-540-89465-0_329
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