干旱地区农业研究Issue(6)：164-168,180,6.
基于相空间重构与RB F神经网络模型的面上干旱预测研究
Research of regional drought forecasting based on phase space reconstruction and RBF neural network model
摘要
Abstract
The vegetation temperature condition index (VTCI)was a real time and quantification drought monitoring method which was suitable to Guanzhong Plain .Based on the earlier research of the drought forecasting of the phase space reconstruction on the VTCI samples in a period of ten days and RBF neural network,further carried out the drought forecasting research of the VTCI by the regional remote sensing .Through analysis of the delay time and reconstruction di-mension of the sample VTCI time series,has determined the phase space dimension in whole region VTCI time series was 7 .Thereby has carried out the phase space reconstruction for the regional VTCI data .Applied the neural network model on the reconstructed VTCI data to do forecast and obtained the forecasting results from early April to middle May of 2009 . The result shown that:The multi-period forecasting results can be welll reflected the feature of the monitoring result, and the absolute error frequency in each ten days period was mainly distributed between -0 .2 to 0 .2 .Applied the Kap-pa Coefficient to evaluate the consistence of the forecasting result with monitoring results:In middle of May was signifi-cant,in first and middle of April was moderate,and in late of April and early of May,the consistence was weak,but the positive consistence was high .These results indicated that this forecasting model can be suitable to the drought forecasting in Guanzhong Plain .关键词
条件植被温度指数/干旱预测/相空间重构/神经网络/RBFKey words
vegetation temperature condition index/drought forecasting/phase space reconstruction/neural net-work/RBF分类
农业科技引用本文复制引用
田苗,王鹏新,侯姗姗,韩萍..基于相空间重构与RB F神经网络模型的面上干旱预测研究[J].干旱地区农业研究,2013,(6):164-168,180,6.基金项目
国家自然科学基金项目（41071235，40871159）；高等学校博士学科点专项科研基金项目 （）
