Application of phase space reconstruction and RBF neural network model in drought forecasting
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DOI：10.7606/j.issn.1000-7601.2011.01.41
Key Words: forecasting  vegetation temperature condition index  phase reconstruction  RBF neural network
Author NameAffiliation
HOU Shanshan College of Information and Electrical Engineering, China Agricultural University, Beijing 00083, China 
WANG Pengxin College of Information and Electrical Engineering, China Agricultural University, Beijing 00083, China 
TIAN Miao College of Information and Electrical Engineering, China Agricultural University, Beijing 00083, China 
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Abstract:
      The chaos theories and phase reconstruction are introduced into drought forecasting, and the vegetation temperature condition index (VTCI) time series are expended to multivariate time series. Based on multi-dimension VTCI time series, phase reconstruction and neural network model are combined to establish the drought forecasting model. The results show that the minimum, maximum, average and standard deviation between predicted value and measured value are very close, all the sample sites predictive values of the relative error are less than 10%, which indicates that the predictive method has high accuracy. And after α=0.05 significance level test, the correlation coefficients between the predicted value and measured value are all around 0.99. The method also demonstrates the utility and efficiency for drought forecasting.
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