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(jié)果,評價BMA改進法的性能。模擬結(jié)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(jié)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
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