本文選題：水文預(yù)報(bào) + 預(yù)報(bào)因子集�。� 參考：《水力發(fā)電學(xué)報(bào)》2017年08期

【摘要】：預(yù)報(bào)因子集是預(yù)報(bào)因子的集合。作為預(yù)報(bào)信息的來(lái)源,因子集對(duì)預(yù)報(bào)結(jié)果有著重要影響,增加因子集包含的預(yù)報(bào)信息量能夠有效地提高預(yù)報(bào)精度。針對(duì)現(xiàn)有方法側(cè)重于對(duì)單個(gè)預(yù)報(bào)因子進(jìn)行研究的不足,本文從整體的角度考慮,提出了基于聯(lián)合互信息的預(yù)報(bào)因子集選取方法。首先介紹了互信息并將其擴(kuò)展到高維情景,引出條件互信息與聯(lián)合互信息,并采用Parzen窗估計(jì)法對(duì)其進(jìn)行計(jì)算;其次以水文預(yù)報(bào)為背景,建立最大聯(lián)合互信息模型,根據(jù)條件互信息進(jìn)行求解,并耦合反向傳播(BP)神經(jīng)網(wǎng)絡(luò)對(duì)計(jì)算結(jié)果進(jìn)行檢驗(yàn);最后對(duì)雅礱江流域?yàn)o寧水文站進(jìn)行實(shí)例計(jì)算,并將計(jì)算結(jié)果與現(xiàn)行方法進(jìn)行比較。結(jié)果表明,新方法能夠?yàn)轭A(yù)報(bào)模型提供更加科學(xué)的輸入,提高模型的預(yù)報(bào)精度。
[Abstract]:The set of prediction factors is the set of prediction factors. As the source of prediction information, factor sets have an important impact on the prediction results, and increasing the amount of forecast information contained in the factor sets can effectively improve the prediction accuracy. In view of the shortcomings of the existing methods which focus on the study of single prediction factors, this paper proposes a method of selecting prediction factor sets based on joint mutual information from the overall point of view. First, the mutual information is introduced and extended to high-dimensional scenarios, then conditional mutual information and joint mutual information are derived, and the Parzen window estimation method is used to calculate the mutual information. Secondly, the maximum joint mutual information model is established based on hydrological forecast. The solution is based on conditional mutual information and coupled with backpropagation neural network to test the calculation results. Finally, a case study of Luning hydrologic station in Yalong River basin is carried out, and the calculation results are compared with the current method. The results show that the new method can provide more scientific input for the prediction model and improve the prediction accuracy of the model.
【作者單位】： 華北電力大學(xué)可再生能源學(xué)院;
【基金】：“十三五”國(guó)家重點(diǎn)研發(fā)計(jì)劃課題(2016YFC0402208) 中央高校基本科研業(yè)務(wù)費(fèi)專項(xiàng)(2016XS46;2016MS51)
【分類號(hào)】：P338
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本文編號(hào)：1897022