發(fā)布時間：2018-03-05 18:44

  本文選題：徑流預(yù)報　切入點：遺傳算法　出處：《水力發(fā)電學(xué)報》2017年08期 　論文類型：期刊論文

【摘要】：徑流預(yù)報對區(qū)域水資源開發(fā)與管理具有重要的作用,當前的研究主要聚焦在先進的算法而忽視了豐富預(yù)報因子對提高徑流預(yù)報精度的貢獻。本研究以涇河徑流為例,將遺傳算法(GA)和回歸支持向量機模型耦合,建立了改進的支持向量機回歸模型(GA-SVR)。預(yù)報變量在常規(guī)預(yù)報因子(降雨與蒸發(fā))的基礎(chǔ)上增加了對徑流影響較強的大氣環(huán)流異常因子。結(jié)果表明,預(yù)測變量未含大氣環(huán)流異常因子的情況下,GA-SVR模型的預(yù)測精度和泛化能力皆優(yōu)于神經(jīng)網(wǎng)絡(luò)模型(ANN);考慮大氣環(huán)流異常因子后,GA-SVR模型預(yù)測精度進一步提高。由此說明,SVR模型耦合GA后可提高月徑流的預(yù)報精度,考慮大氣環(huán)流異常因子后其預(yù)測精度可進一步提高。
[Abstract]:Runoff forecasting plays an important role in the development and management of regional water resources. The current research focuses on advanced algorithms and neglects the contribution of rich forecasting factors to the improvement of runoff forecasting accuracy. The genetic algorithm (GA) is coupled with the regression support vector machine (RSVM) model. An improved support vector machine regression model (GA-SVR) was established. Based on the conventional prediction factors (rainfall and evaporation), the anomalous factors of atmospheric circulation with strong influence on runoff were added to the prediction variables. The prediction accuracy and generalization ability of GA-SVR model without atmospheric circulation anomaly factor is better than that of neural network model, and the prediction accuracy of GA-SVR model is further improved after considering the atmospheric circulation anomaly factor. The forecasting accuracy of monthly runoff can be improved by coupling GA with the model. Considering the anomalous factor of atmospheric circulation, the prediction accuracy can be further improved.
【作者單位】： 西安理工大學(xué)水利水電學(xué)院西北旱區(qū)生態(tài)水利工程國家重點實驗室培育基地;
【基金】：陜西省水利科技計劃項目(2017slkj-19);陜西省水利科技計劃項目(2016slkj-8) 國家自然科學(xué)基金(91325201) 水利部公益項目(201501058)
【分類號】：P338.2

【相似文獻】

相關(guān)期刊論文 前1條

1 潘華盛,張桂華,袁美英;大氣環(huán)流異常與黑龍江省雨澇及全國雨型[J];黑龍江氣象;2001年03期

，

本文編號：1571485