發(fā)布時間：2018-02-24 16:50

  本文關鍵詞： 數據處理 回歸 整合 作物需水量 貝葉斯理論 硬數據 軟數據 先驗信息　出處：《農業(yè)工程學報》2017年09期 　論文類型：期刊論文

【摘要】：作物需水量是灌溉工程規(guī)劃、設計和管理的重要基礎數據,充分利用多源數據和先驗知識,快速經濟地獲取精度較高的區(qū)域作物需水量對于區(qū)域水資源的優(yōu)化配置具有重要意義。為精確預測作物需水量,該文以長系列實際監(jiān)測和校核作物系數后計算得到的作物需水量為硬數據,利用硬數據確定獲得最大熵的約束條件,根據軟數據獲取渠道的不同(部分年份缺失的站點數據、文獻中獲得的數據、利用灌溉試驗數據庫中的作物需水量資料,采用協(xié)同克立格方法獲得的數據、考慮主要地形因子和主要氣象要素的影響,采用主成分分析和地理加權回歸(geographically weighted regression,GWR)方法獲得作物需水量數據以及遙感數據),提出不同來源軟數據的概率密度函數表達方法,采用貝葉斯最大熵(Bayesian maximum entropy,BME)方法對不同來源的作物需水量信息進行有機整合。結果表明:除硬數據+文獻軟數據外,其他數據整合呈現一致結果。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高值區(qū),山東需水量相對較高,冀東北的樂亭、唐山附近有相對低值區(qū)。除硬數據+文獻軟數據比不整合的精度低9.41%外,其他軟數據源均可不同程度地提高整合效果,硬數據+克立格軟數據、硬數據+GWR軟數據和硬數據+除文獻數據外的其他軟數據分別比不整合的精度提高85.33%、85.75%和91.69%。對考慮地形、氣象等要素的多源數據進行整合可更好地反映冬小麥作物需水量空間分布的細節(jié),顯著提高估算精度,為稀疏監(jiān)測站點地區(qū)水土資源的精準管理和優(yōu)化配置提供數據支撐。
[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】： 西北農林科技大學水利與建筑工程學院;中國農業(yè)科學院農田灌溉研究所;中國農業(yè)大學水利與土木工程學院;
【基金】：水利部公益性行業(yè)科研專項經費項目(201501016) 國家自然科學基金(51609245) 中央級科研院所基本科研業(yè)務費專項(FIRI2016-09) 河南省基礎與前沿技術研究(162300410168)
【分類號】：O212.8;S311

【相似文獻】

相關期刊論文 前1條

1 王世民;;對計算作物蒸散量的幾種方法的模糊綜合評判[J];河北農業(yè)大學學報;1988年04期

，

本文編號：1530998