农业工程学报2017，Vol.33Issue(9)：99-106,8.DOI:10.11975/j.issn.1002-6819.2017.09.013
基于贝叶斯最大熵和多源数据的作物需水量空间预测
Spatial prediction of crop water requirement based on Bayesian maximum entropy and multi-source data
摘要
Abstract
Crop water requirement is an important basic data for planning, design and management of irrigation engineering. Obtaining high-precision regional crop water requirement prediction using multi-source data and the priori knowledge has great significance for optimal allocation of regional water resources. In the paper, multisource data was integrated using the Bayesian maximum entropy (BME) method for crop water requirement prediction. A long series of crop water requirement measured and calculated by using the crop coefficients adjusted for actual measurement, were taken as the hard data. The soft data included the missing data in partial years for some stations, literature data, the Kriging interpolation data considering the main influence elements of crop water requirement, the crop water requirement data based on the principal component analysis (PCA) and geographically weighted regression (GWR) method, and the remote sensing data. For the soft data from different sources, the expression method of probability density function was put forward and the crop water requirement information from different sources was well integrated using the BME method. Hard data for the period of 1954-2013were collected from measurements from the irrigation stations in North China. Soft data for winter wheat in North China were also collected by searching literatures and the others. The results showed that spatial distribution of crop water requirement in North China was almost consistent for the hard data, hard data + Kriging soft data, hard data + GWR soft data and hard data + the soft data except for literature data. In the southern Henan had smaller crop water requirement, but the middle part (northern part of the Yellow River) of the North China was relatively high. The crop water requirement was relatively high in Shandong but low in the northeast of Hebei such as Leting, Tangshan. The results from hard data + literature soft data were slightly different from the others and the difference might be because the time periods used were different. In general, the integration accuracy of hard data + literature soft data was 9.41% lower than that based on hard data only. Hard data integrated with the other soft data could improve the integration effect. In particular, the integration accuracy of hard data + Kriging soft data, hard data + GWR soft data and hard data + the soft data except for literature data increased by 85.33%, 85.75% and 91.69%, respectively. The integration of multi-source data through considering the terrain, meteorological factors and etc, can could better reflect the spatial distribution of crop water requirements for winter wheat, and significantly improve the estimation accuracy of crop water requirement for winter wheat. The presented method provided the most important basic data for the precise management and optimization of water and soil resources in the region with sparse monitoring stations. In the paper, we need pay attention to some questions in the soft data processing. The partially missing data of some stations need amend the variance calculation results. In order to avoid the agglomeration phenomenon, the selection of interpolation data need adopt the method of random sampling, and at the same time, the distance between adjacent samplings must be limited, should not be too far or near, and 20 km was advisable. In order to avoid too big error and uncertainty, the literature data must be screened and pretreatment, otherwise, the integration effect may be affected.关键词
数据处理/回归/整合/作物需水量/贝叶斯理论/硬数据/软数据/先验信息Key words
data processing/regression/integration/crop water requirement/BME/hard data/soft data/prior information分类
数理科学引用本文复制引用
王景雷,康绍忠,孙景生,陈智芳,宋妮..基于贝叶斯最大熵和多源数据的作物需水量空间预测[J].农业工程学报,2017,33(9):99-106,8.基金项目
水利部公益性行业科研专项经费项目(201501016) （201501016）
国家自然科学基金(51609245) （51609245）
中央级科研院所基本科研业务费专项(FIRI2016-09) （FIRI2016-09）
河南省基础与前沿技术研究(162300410168) （162300410168）
