基于k-最近鄰篩選的BMA集合預報模型研究
發(fā)布時間：2018-10-20 14:59
【摘要】：針對冗余訓練樣本會降低BMA參數(shù)求解效率與精度問題,本文提出在BMA運算之前采用k-最近鄰(k-nearest neighbor)算法篩選有價值訓練樣本,并用于BMA參數(shù)求解的改進模型。模擬試驗在淮河王家壩站進行,分別以k-最近鄰篩選、不篩選兩種方案為BMA提供訓練樣本,統(tǒng)計分析兩種方案中王家壩站流量模擬結果,評價BMA改進法的性能。模擬結果顯示,采用k-最近鄰樣本篩選方法后,BMA模型對洪水過程以及洪峰的預報精度提升明顯;概率預報結果的離散程度降低的同時,可靠性程度獲得提升。k-最近鄰樣本篩選方法的引入,能夠有效去除BMA模型訓練樣本中的冗余數(shù)據(jù),以少量的樣本獲得更可靠的模型參數(shù),改善集合預報性能。
[Abstract]:In view of the problem that redundant training samples can reduce the efficiency and precision of BMA parameter solving, this paper presents an improved model of selecting valuable training samples by using k- nearest neighbor (k-nearest neighbor) algorithm before BMA operation, which can be used to solve BMA parameters. The simulation test was carried out at the Wangjiaba Station of Huaihe River. The two schemes were screened by the nearest neighbor, and the two schemes were not screened to provide training samples for the BMA. The flow simulation results of the Wangjiaba Station in the two schemes were statistically analyzed, and the performance of the improved BMA method was evaluated. The simulation results show that the accuracy of BMA model for flood process and Hong Feng is improved obviously, and the dispersion of probabilistic forecast results is reduced. The introduction of K-nearest neighbor sample screening method can effectively remove redundant data from training samples of BMA model, obtain more reliable model parameters with a small number of samples, and improve the performance of ensemble prediction.
【作者單位】： 淮河水利委員會水文局(信息中心);河海大學水文水資源學院;
【基金】：國家重點研發(fā)計劃項目(2016YFC0400909) 國家自然科學基金項目(41130639,51179045,41101017,41201028)
【分類號】：P338
本文編號：2283506
[Abstract]:In view of the problem that redundant training samples can reduce the efficiency and precision of BMA parameter solving, this paper presents an improved model of selecting valuable training samples by using k- nearest neighbor (k-nearest neighbor) algorithm before BMA operation, which can be used to solve BMA parameters. The simulation test was carried out at the Wangjiaba Station of Huaihe River. The two schemes were screened by the nearest neighbor, and the two schemes were not screened to provide training samples for the BMA. The flow simulation results of the Wangjiaba Station in the two schemes were statistically analyzed, and the performance of the improved BMA method was evaluated. The simulation results show that the accuracy of BMA model for flood process and Hong Feng is improved obviously, and the dispersion of probabilistic forecast results is reduced. The introduction of K-nearest neighbor sample screening method can effectively remove redundant data from training samples of BMA model, obtain more reliable model parameters with a small number of samples, and improve the performance of ensemble prediction.
【作者單位】： 淮河水利委員會水文局(信息中心);河海大學水文水資源學院;
【基金】：國家重點研發(fā)計劃項目(2016YFC0400909) 國家自然科學基金項目(41130639,51179045,41101017,41201028)
【分類號】：P338
【相似文獻】
相關期刊論文 前4條
1 盧迪;彭勇;徐煒;周惠成;;氣象集合預報在水文領域中的應用研究進展[J];南水北調與水利科技;2014年02期
2 陸桂華;吳娟;吳志勇;;水文集合預報試驗及其研究進展[J];水科學進展;2012年05期
3 李巖;胡軍;王金星;劉松;章四龍;;河流集合預報方法(ESP)在水資源中長期預測中的應用研究[J];水文;2008年01期
4 ;[J];;年期
相關會議論文 前1條
1 包紅軍;趙琳娜;梁莉;;基于水文集合模式的流域洪澇預報技術[A];第十四屆中國科協(xié)年會第14分會場：極端天氣事件與公共氣象服務發(fā)展論壇論文集[C];2012年
相關重要報紙文章 前1條
1 本報記者 張妍;為水庫蓄水提供決策依據(jù)[N];中國氣象報;2011年
相關碩士學位論文 前1條
1 油芳芳;耦合ECMWF降雨集合預報的水庫優(yōu)化調度研究[D];大連理工大學;2014年
，本文編號：2283506
本文鏈接：http://sikaile.net/kejilunwen/shuiwenshuili/2283506.html
最近更新
教材專著
