| 贾浩,王振华,张金珠,李文昊,任作利,贾哲诚.基于主成分分析和Copula函数的灌溉水利用系数影响因素研究[J].干旱地区农业研究,2020,38(6):167~175 |
| 基于主成分分析和Copula函数的灌溉水利用系数影响因素研究 |
| Study on factors influencing irrigation water use coefficient based on principal component analysis and Copula function |
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| DOI：10.7606/j.issn.1000-7601.2020.06.23 |
| 中文关键词: 灌溉水 有效利用系数 影响因素 首尾法 主成分分析法 PCA-Copula |
| 英文关键词:irrigation water effective utilization coefficient influencing factors head and tail method principal component analysis method PCA-Copula |
| 基金项目:新疆生产建设兵团重点领域创新团队项目“干旱区滴灌节水科技创新团队（2019CB004）” |
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| 中文摘要: |
| 以新疆生产建设兵团第十二师中型灌区为研究对象，采用首尾法测算2018年和2019年4个样点灌区的灌溉水利用系数，在采用主成分分析法对指标体系降维处理的基础上，用Copula函数构建PCA-Copula评价分析方法，对灌溉水利用系数各影响因素的影响程度和影响规律进行计算分析。结果表明：两年测算数据表明，4个样点灌区灌溉水利用系数都在0.63以上，且最大值达到0.668，分别高于同期全疆、全国平均水平5.05%、10.15%；主成分分析得出，渠道衬砌率（0.944）、滴灌灌溉面积比（0.746）、作物需水量（0.635）、实际灌溉面积（0.734）等都具有超过60%的正贡献率，而葡萄种植比（-0.586）和灌区毛灌溉用水量（-0.645）等具有超过58%的负贡献率。利用PCA-Copula分析评估方法得出，作物种植比例和节水灌溉工程状况对十二师中型灌区灌溉水有效利用系数影响显著（P<0.05），其中葡萄净灌溉定额和灌区毛灌溉用水量对灌溉水有效利用系数的影响极其显著（P<0.01），相关系数分别为0.875、0.742，同时利用Spearman等级相关系数检验法和线型回归来检验PCA-Copula评价法与熵值法的密切程度，检验结果其相关系数分别为 0.87（P＜0.05）和0. 52（P＜0.001），表明PCA-Copula评价方法适用于研究灌溉水利用系数影响因素。 |
| 英文摘要: |
| Taking the medium\|sized irrigation area of the 12th Division of Xinjiang Construction Corps as the research object, we used the first\|to\|tail method to calculate the irrigation water utilization coefficient of the four sample irrigation areas in 2018 and 2019. Based on the principal component analysis method for dimensionality reduction of the index system，the Copula function was used to construct the PCA\|Copula evaluation and analysis method to calculate and analyze each influence factor, its degree as well as its law, of water utilization coefficient. The results showed that according to the two\|year calculation and analysis, the irrigation water utilization coefficients of the four sample irrigation areas were all above 0.63, and the maximum value reached 0.668, which was higher than the Xinjiang and national averages of 5.05% and 10.15%, respectively, during the same period. Principal component analysis showed that the channel lining rate (0.944), drip irrigation area ratio (0.746), crop water requirement (0.635), actual irrigation area (0.734), etc. All had a positive contribution rate more than 60%, while the grape growing ratio (-0.586) and irrigation area gross irrigation water consumption (-0.645) had a negative contribution rate of more than 58%. Use the PCA-Copula analysis and evaluation method it was to conclude that the crop planting ratio and water\|saving irrigation project status had a significant influence on the effective utilization coefficient of irrigation water in the Twelve Division medium\|sized irrigation area (P<0.05). The effect of irrigation water effective utilization coefficient was extremely significant (P<0.01), with the correlation coefficients 0.875 and 0.742, respectively. At the same time, the Spearman rank correlation coefficient test and linear regression were used to test the closeness of the PCA-Copula evaluation method and the entropy method. The test results were that the correlation coefficients were 0.87 (P<0.05) and 0.52 (P<0.001), it showed that the PCA-Copula evaluation method was suitable for studying the influencing factors of irrigation water utilization coefficient. |
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