CAJ | 학술논문

噶尔河流域位于新疆塔里木盆地西南缘，流域严重缺水，直接影响到流域的农牧业生产，因此需要对其径流量做出相对准确的预测。选用喀什噶尔河流域1956年-2000年实测年径流量，运用小波分解方法揭示径流的趋势性和周期性，并利用周期性结果为径流预测提供数据输入依据，建立径向基神经网络预测模型，与传统径向基神经网络预测模型相比，精度更高，能够更加准确的预测喀什噶尔河流域年径流量。
갈이하류역위우신강탑리목분지서남연，류역엄중결수，직접영향도류역적농목업생산，인차수요대기경류량주출상대준학적예측。선용객십갈이하류역1956년-2000년실측년경류량，운용소파분해방법게시경류적추세성화주기성，병이용주기성결과위경류예측제공수거수입의거，건립경향기신경망락예측모형，여전통경향기신경망락예측모형상비，정도경고，능구경가준학적예측객십갈이하류역년경류량。
Kashgar River is located in the southwestern margin of the Tarim Basin in Xinjiang ,The water shortages di-rectly affect agricultural productions ,so it is necessary to make relatively accurate predictions of the river runoff .Here , the measured annual runoff data of Kashgar River from 1956 to 2000 was chosen in analysing the trends and different cy-cles of the runoff by using wavelet decomposition .The results provided input data for the runoff forecasting ,based on which an annual runoff model of RBF neural network was established .The study proved that RBF neural network predic-tion model had higher accuracy than that of conventional models ,and it could more accurately predict the annual runoff of Kashgar River .