人民黄河
人民黃河
인민황하
Yellow River
2014年
7期
29-31
,共3页
生命旋回 - Markov 链模型%预测%径流量%黄河上游
生命鏇迴 - Markov 鏈模型%預測%徑流量%黃河上遊
생명선회 - Markov 련모형%예측%경류량%황하상유
life cycle-Markov chain model%prediction%runoff%upper Yellow River
为了解决生命旋回模型无法反映径流序列随机性的问题，将生命旋回模型与 Markov 链模型结合，建立了生命旋回- Markov 链组合预测模型。该模型的特点是用生命旋回模型模拟预测径流量序列的趋势项，用 Markov 链模型对径流残差序列进行修正。应用该方法对黄河上游唐乃亥水文站的径流量预测结果表明：汛期预测平均相对误差为18.07%，但是有些月份误差较大；枯期预测的平均相对误差为8.26%。
為瞭解決生命鏇迴模型無法反映徑流序列隨機性的問題，將生命鏇迴模型與 Markov 鏈模型結閤，建立瞭生命鏇迴- Markov 鏈組閤預測模型。該模型的特點是用生命鏇迴模型模擬預測徑流量序列的趨勢項，用 Markov 鏈模型對徑流殘差序列進行脩正。應用該方法對黃河上遊唐迺亥水文站的徑流量預測結果錶明：汛期預測平均相對誤差為18.07%，但是有些月份誤差較大；枯期預測的平均相對誤差為8.26%。
위료해결생명선회모형무법반영경류서렬수궤성적문제，장생명선회모형여 Markov 련모형결합，건립료생명선회- Markov 련조합예측모형。해모형적특점시용생명선회모형모의예측경류량서렬적추세항，용 Markov 련모형대경류잔차서렬진행수정。응용해방법대황하상유당내해수문참적경류량예측결과표명：신기예측평균상대오차위18.07%，단시유사월빈오차교대；고기예측적평균상대오차위8.26%。
In order to resolve the problem that the life cycle model can not reflect the fluctuation and random characteristics,the life cycle-Markov chain model which combines life cycle model with Markov chain model was built. The characteristic of the model was to predict the trend of runoff series by using life cycle model and amend runoff residual sequence by using Markov chain-model. The runoff prediction of Tangnaihai Hydrological Station in the upper Yellow River shows that the average accuracy of flood period prediction is 81. 93% ,however some months′precision is less ac-curate,which value is less than 80% ;The average accuracy of dry period is 91. 74% ,which qualified rate is 100% .
