| 引用本文: | 王 科,李银坤,郑文刚,等.基于主成分分析的温室内水面蒸发量估算模型构建及验证[J].灌溉排水学报,2023,42(5):60-66. |
| WANG Ke,LI Yinkun,ZHENG Wengang,et al.基于主成分分析的温室内水面蒸发量估算模型构建及验证[J].灌溉排水学报,2023,42(5):60-66. |
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| 摘要: |
| 【目的】估算温室无风环境下的水面蒸发量（Ep）。【方法】基于温室内2020年与2022年3—7月的实测水面蒸发量（Ep）与气象数据，采用主成分分析法对Ep的影响因素进行分析，利用提取出的主成分与Ep构建多元回归模型，并对估算结果进行验证。【结果】试验期间Ep随试验时间的延长呈上升趋势，2020年3月与2022年3月的Ep日平均值分别为1.84 mm与1.94 mm，6月分别增加至3.77 mm与5.15 mm；辐射、湿度对温室无风环境下水面蒸发量的影响占主要地位，其中光合有效辐射与水面蒸发量的相关性最高，相关系数为0.852（P＜0.01），其次为太阳辐射与湿度，相关系数分别为0.811与-0.770（P＜0.01）。第一主成分的太阳辐射、光合有效辐射以及湿度对水面蒸发量影响较大，特征值为4.44，其中太阳辐射对水面蒸发量影响最明显，得分系数最高，为0.328；湿度与光合有效辐射次之，得分系数分别为0.311与-0.321。基于主成分分析结果建立了水面蒸发量估算模型，水面蒸发量估算值与实测值显著正相关（P＜0.01），方程相关系数R2为0.908，MBE为0.10，RMSE为0.48 mm/d，一致性指数较高（d=0.94）。【结论】在温室无风环境下太阳辐射、光合有效辐射与湿度对水面蒸发量影响较高。 |
| 关键词: 温室；水面蒸发量；气象要素；多重共线性；主成分分析 |
| DOI：10.13522/j.cnki.ggps.2022543 |
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| Construction and Validation of a Model for Estimating Surface Water Evaporation in Greenhouse Based on Principal Component Analysis |
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WANG Ke, LI Yinkun, ZHENG Wengang, LIU Meiying,
WU Jiale, JI Yuru, CHEN Fei, HOU Shenglin
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1. Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China;
2. Inner Mongolia Key Laboratory of Soil Quality and Nutrient Resource, College of Grassland, Resources and Environment,
Inner Mongolia Agricultural University, Hohhot 010018, China; 3.College of Horticulture, China Agricultural University,
Beijing 100094, China; 4. Hebei Academy of Agriculture and Forestry Sciences, Shijiazhuang 050051, China
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| Abstract: |
| 【Objective】Surface water evaporation (Ep) in greenhouse is often used as a reference in irrigation management. The purpose of this paper is to present a model to estimate it.【Method】The model was derived based on meteorological data and pan-evaporation measured from March to July in 2020 and 2022. The relationship between Ep and meteorological data was analyzed using the principal component method, from which a multiple linear regression model was developed to estimate Ep. 【Result】Ep increased as time elapsed, with its average increasing from the range of 1.84~1.94 mm in March to the range of 3.77~5.15 mm in June in both 2020 and 2022. Ep was influenced by radiation and relative humidity the most. Photosynthetically active radiation had the highest correlation with Ep, with their correlation coefficient being 0.852 (P<0.01), followed by solar radiation and relative humidity with their associated correlation coefficient being 0.811 and -0.770, respectively (P<0.01). The first principal component of solar radiation, photosynthetically active radiation, and relative humidity has a great effect on Ep, with the eigenvalue being 4.44. The solar radiation affected Ep significantly, with the highest score coefficient (0.328), followed by relative humidity and photosynthetically active radiation, whose score coefficients were 0.311 and -0.321, respectively. Principal component analysis and verification showed that the estimated Ep using the proposed model agreed well with measured data, with P<0.01, R2=0.908, MBE=0.10, RMSE=0.48 mm/d, and the consistency index=0.94. 【Conclusion】Solar radiation, photosynthetically active radiation, and relative humidity are the main factors influencing Ep in the greenhouse. The multiple linear regression model derived from the principal component analysis is accurate and can provide real-time estimates of surface water evaporation in the greenhouse. |
| Key words: greenhouse; pan evaporation; meteorological factors; multiple collinearity; principal component analysis |
