干旱地区农业研究2011，Vol.29Issue(1)：224-230,7.
基于相空间重构与RBF神经网络的干旱预测模型
Application of phase space reconstruction and RBF neural network model in drought forecasting
摘要
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.关键词
预测/条件植被温度指数/相空间重构/径向基函数神经网络Key words
forecasting/ vegetation temperature condition index/ phase reconstruction/ RBF neural network分类
农业科技引用本文复制引用
侯姗姗,王鹏新,田苗..基于相空间重构与RBF神经网络的干旱预测模型[J].干旱地区农业研究,2011,29(1):224-230,7.基金项目
国家自然科学基金项目(40871159,40571111,40371083) （40871159,40571111,40371083）
国家高技术研究发展计划课题(2007AA12Z139) （2007AA12Z139）
欧盟FP7项目(Call FP7-ENV-2007-1212921) （Call FP7-ENV-2007-1212921）
