CAJ | 학술논문

条件植被温度指数（VTCI）是一种适合关中平原的近实时定量化的干旱监测方法，在前期基于以旬为单位的VTCI样本点上相空间重构与RBF神经网络干旱预测研究的基础上，进一步进行了VTCI遥感面上的干旱预测研究。通过分析样本点VTCI时间序列的延迟时间和重构维数，确定整个面上VTCI时间序列相空间维数为7，从而对面上VTCI数据进行了相空间重构。对重构后的VTCI数据应用RBF神经网络模型预测得到了2009年4月上旬到5月中旬的VTCI预测结果。结果表明，多旬预测结果都较好地反映了监测结果的特征，各旬预测结果的绝对误差频数分布主要集中在-0．2到0．2之间。应用Kappa系数评价预测结果与监测结果的一致性程度：5月中旬为显著，4月上旬和中旬为中度，4月下旬和5月上旬的一致性为弱，但阳性一致率较高。该模型的面上预测精度较好，适合关中平原的干旱预测研究。
조건식피온도지수（VTCI）시일충괄합관중평원적근실시정양화적간한감측방법，재전기기우이순위단위적VTCI양본점상상공간중구여RBF신경망락간한예측연구적기출상，진일보진행료VTCI요감면상적간한예측연구。통과분석양본점VTCI시간서렬적연지시간화중구유수，학정정개면상VTCI시간서렬상공간유수위7，종이대면상VTCI수거진행료상공간중구。대중구후적VTCI수거응용RBF신경망락모형예측득도료2009년4월상순도5월중순적VTCI예측결과。결과표명，다순예측결과도교호지반영료감측결과적특정，각순예측결과적절대오차빈수분포주요집중재-0．2도0．2지간。응용Kappa계수평개예측결과여감측결과적일치성정도：5월중순위현저，4월상순화중순위중도，4월하순화5월상순적일치성위약，단양성일치솔교고。해모형적면상예측정도교호，괄합관중평원적간한예측연구。
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 .