發(fā)布時(shí)間：2018-02-24 16:50

  本文關(guān)鍵詞： 數(shù)據(jù)處理 回歸 整合 作物需水量 貝葉斯理論 硬數(shù)據(jù) 軟數(shù)據(jù) 先驗(yàn)信息　出處：《農(nóng)業(yè)工程學(xué)報(bào)》2017年09期 　論文類(lèi)型：期刊論文

【摘要】：作物需水量是灌溉工程規(guī)劃、設(shè)計(jì)和管理的重要基礎(chǔ)數(shù)據(jù),充分利用多源數(shù)據(jù)和先驗(yàn)知識(shí),快速經(jīng)濟(jì)地獲取精度較高的區(qū)域作物需水量對(duì)于區(qū)域水資源的優(yōu)化配置具有重要意義。為精確預(yù)測(cè)作物需水量,該文以長(zhǎng)系列實(shí)際監(jiān)測(cè)和校核作物系數(shù)后計(jì)算得到的作物需水量為硬數(shù)據(jù),利用硬數(shù)據(jù)確定獲得最大熵的約束條件,根據(jù)軟數(shù)據(jù)獲取渠道的不同(部分年份缺失的站點(diǎn)數(shù)據(jù)、文獻(xiàn)中獲得的數(shù)據(jù)、利用灌溉試驗(yàn)數(shù)據(jù)庫(kù)中的作物需水量資料,采用協(xié)同克立格方法獲得的數(shù)據(jù)、考慮主要地形因子和主要?dú)庀笠氐挠绊?采用主成分分析和地理加權(quán)回歸(geographically weighted regression,GWR)方法獲得作物需水量數(shù)據(jù)以及遙感數(shù)據(jù)),提出不同來(lái)源軟數(shù)據(jù)的概率密度函數(shù)表達(dá)方法,采用貝葉斯最大熵(Bayesian maximum entropy,BME)方法對(duì)不同來(lái)源的作物需水量信息進(jìn)行有機(jī)整合。結(jié)果表明:除硬數(shù)據(jù)+文獻(xiàn)軟數(shù)據(jù)外,其他數(shù)據(jù)整合呈現(xiàn)一致結(jié)果。華北地區(qū)冬小麥作物需水量在豫南地區(qū)較小,中部地區(qū)黃河北岸有連片的相對(duì)高值區(qū),山東需水量相對(duì)較高,冀東北的樂(lè)亭、唐山附近有相對(duì)低值區(qū)。除硬數(shù)據(jù)+文獻(xiàn)軟數(shù)據(jù)比不整合的精度低9.41%外,其他軟數(shù)據(jù)源均可不同程度地提高整合效果,硬數(shù)據(jù)+克立格軟數(shù)據(jù)、硬數(shù)據(jù)+GWR軟數(shù)據(jù)和硬數(shù)據(jù)+除文獻(xiàn)數(shù)據(jù)外的其他軟數(shù)據(jù)分別比不整合的精度提高85.33%、85.75%和91.69%。對(duì)考慮地形、氣象等要素的多源數(shù)據(jù)進(jìn)行整合可更好地反映冬小麥作物需水量空間分布的細(xì)節(jié),顯著提高估算精度,為稀疏監(jiān)測(cè)站點(diǎn)地區(qū)水土資源的精準(zhǔn)管理和優(yōu)化配置提供數(shù)據(jù)支撐。
[Abstract]:Crop water demand is an important basic data for irrigation engineering planning, design and management, making full use of multi-source data and prior knowledge. Rapid and economical acquisition of regional crop water demand with high precision is of great significance for the optimal allocation of regional water resources. In this paper, the crop water demand calculated after a long series of actual monitoring and checking of crop coefficients is used as hard data, and the constraint conditions for obtaining maximum entropy are determined by hard data. The data obtained in the literature, using the crop water demand data in the irrigation experiment database, and the data obtained by using the cooperative Kriging method, take into account the influence of the main terrain factors and the main meteorological factors. Crop water demand data and remote sensing data were obtained by principal component analysis (PCA) and geographical weighted weighted regression (GWR), and the probability density function (PDF) method of soft data from different sources was proposed. The Bayesian maximum entropy Bayesian maximum entropyBME method is used to integrate the crop water demand information from different sources. The results of other data integration show that the winter wheat crop water demand in North China is small in the south of Henan, there are relative high value areas on the north bank of the Yellow River in the central region, the water demand in Shandong is relatively high, and the water demand is relatively high in Leting in the northeast of Hebei Province. There is a relatively low value area near Tangshan. With the exception of hard data literature, the accuracy of soft data is 9.41% lower than that of unconformity, other soft data sources can improve the integration effect in varying degrees. The hard data GWR soft data and the hard data except the literature data are 85.33% higher than the unconformity precision and 91.69% respectively. The integration of multi-source data of meteorological elements can better reflect the details of spatial distribution of winter wheat crop water demand, improve the accuracy of estimation, and provide data support for accurate management and optimal allocation of soil and water resources in sparse monitoring sites.
【作者單位】： 西北農(nóng)林科技大學(xué)水利與建筑工程學(xué)院;中國(guó)農(nóng)業(yè)科學(xué)院農(nóng)田灌溉研究所;中國(guó)農(nóng)業(yè)大學(xué)水利與土木工程學(xué)院;
【基金】：水利部公益性行業(yè)科研專(zhuān)項(xiàng)經(jīng)費(fèi)項(xiàng)目(201501016) 國(guó)家自然科學(xué)基金(51609245) 中央級(jí)科研院所基本科研業(yè)務(wù)費(fèi)專(zhuān)項(xiàng)(FIRI2016-09) 河南省基礎(chǔ)與前沿技術(shù)研究(162300410168)
【分類(lèi)號(hào)】：O212.8;S311

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本文編號(hào)：1530998