Abstract
FS-BP model was used to try to predict precipitation. The last six years’ precipitation were selected as input variable and the next year’s precipitation as output variable. The results show that the mean relative error of the prediction is 2.92%. T-test and regression analysis indicates that the predicted value differs just slightly from the observed value and their correlation coefficient was 0.9901.The FS-BP model is quite higher than BP model in accuracy and stability, and serves as useful tool in further research on prediction of precipitation.
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References
Xing, N.: Agricultural Research in Arid Northern. China Agriculture Press, Beijing (2002)
Xie, J., Zeng, Q., Ji, Z.: The progress of meteorological statistics forecast of China in recent 30 years. Meteorological Technology 2, 67–69 (2003)
Penev, K., Littlefair, G.: Free Search-a comparative analysis. Information Sciences 172, 173–193 (2005)
Wang, B., Zhang, Z., Zhang, G.: Free search: A novel algorithm for optimizing irrigation schedule. Advance in Water Science 5, 738–741 (2008)
Zhang, G., Zhang, Z., Shao, G., et al.: Optimization model for discharge distribution of irrigation channels based on particle swarm optimizer. Journal of Hydraulic Engineering 8, 1004–1008 (2006)
Bo, Y., Jiang, J., Sun, Y.: Research Progress on Particle Swarm Optimization. Journal of Inner Mongolia University for Nationalities (Natural Sciences) 6, 630–633 (2010)
Bian, X., Mi, L.: Development on genetic algorithm theory and its applications. Application Research of Computers 7, 2425–2430 (2010)
Wang, J.: Development of Research on Differential Evolution Algorithm. Journal of Hunan First Normal University 6, 144–147 (2009)
Li, H., Yang, G., Wang, X.: GA-Based BP Network Used in Rainfall Prediction. Journal of Qingdao University (Engineering & Technology Edition) 1, 10–14 (2010)
Li, X., Zhang, Z., Yang, J., et al.: Prediction of slope infiltration based on artificial neural networks by free search. Transactions of the CSAE 12, 193–197 (2009)
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© 2011 Springer-Verlag Berlin Heidelberg
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Yin, GH., Gu, J., Zhang, FS., Shen, YJ., Liu, Zx. (2011). Prediction of Precipitation Based on Artificial Neural Networks by Free Search. In: Jin, D., Lin, S. (eds) Advances in Computer Science, Intelligent System and Environment. Advances in Intelligent and Soft Computing, vol 105. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23756-0_61
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DOI: https://doi.org/10.1007/978-3-642-23756-0_61
Publisher Name: Springer, Berlin, Heidelberg
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