| Research of regional drought forecasting based on phase space reconstruction and RBF neural network model |
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| DOI：10.7606/j.issn.1000-7601.2013.06.029 |
| Key Words: vegetation temperature condition index drought forecasting phase space reconstruction neural network RBF |
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| 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 R
BF 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 dimension 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 netwo
rk 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 Kappa Coefficient to evaluate the
consistence of the forecasting result with monitoring results: In middle of May was significant, 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. |