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Study on Ann-Based Multi-Step Prediction Model of Short-Term Climatic Variation


doi: 10.1007/s00376-000-0051-4

  • In the context of 1905-1995 series from Nanjing and Hangzhou, study is undertaken of establishing a predictive model of annual mean temperature in 1996-2005 to come over the Changjiang (Yangtze River) delta region through mean generating function and artificial neural network in combination. Results show that the established model yields mean error of 0.45℃ for their absolute values of annual mean temperature from 10 yearly independent samples (1986-1995) and the difference between the mean predictions and related measurements is 0.156℃. The developed model is found superior to a mean generating function regression model both in historical data fitting and independent sample prediction.
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Manuscript History

Manuscript received: 10 January 2000
Manuscript revised: 10 January 2000
通讯作者: 陈斌, bchen63@163.com
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    沈阳化工大学材料科学与工程学院 沈阳 110142

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Study on Ann-Based Multi-Step Prediction Model of Short-Term Climatic Variation

  • 1. Jiangsu Research Institute of Meteorological Sciences; Nanjing; 210008,Jiangsu Research Institute of Meteorological Sciences; Nanjing; 210008,Nanjing Institute of Meteorology; 210044

Abstract: In the context of 1905-1995 series from Nanjing and Hangzhou, study is undertaken of establishing a predictive model of annual mean temperature in 1996-2005 to come over the Changjiang (Yangtze River) delta region through mean generating function and artificial neural network in combination. Results show that the established model yields mean error of 0.45℃ for their absolute values of annual mean temperature from 10 yearly independent samples (1986-1995) and the difference between the mean predictions and related measurements is 0.156℃. The developed model is found superior to a mean generating function regression model both in historical data fitting and independent sample prediction.

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