利用MARS估算不同气象要素组合下的参考作物蒸散量

Estimation of Reference Crop Evapotranspiration Under Different Combination of Meteorological Elements Using Multivariate Adaptive Regression Splines

    摘要
  • 摘要: 建立精确预测参考作物蒸散量（ET0）的计算模型对区域水资源规划和灌溉调度设计具有重要意义。聚焦评估多元自适应回归样条模型（multivariate adaptive regression splines，MARS）计算每日ET0的性能。首先，将Penman-Monteith方程计算的ET0作为标准值；然后，利用中国新疆维吾尔自治区伊犁哈萨克自治州伊宁站1996—2015年逐日气象数据，建立14种不同气象参数组合下的MARS模型并计算ET0；最后，将结果与广义回归神经网络（general regression neural network，GRNN）、支持向量机（support vector machine，SVM）及基于温度、传质、辐射和气象参数的10个经验方程进行比较。结果表明，MARS、GRNN和SVM计算ET0的精度均高于经验方程，整体上MARS性能最好、精度最高，而SVM略优于GRNN。

     

    Abstract:
      Objectives  Building a calculation model for accurate reference crop evapotranspiration (ET0) prediction is important for regional water resource planning and irrigation scheduling design. This study focused on evaluating the performance of the multivariate adaptive regression spline model (MARS) in calculating the daily ET0.
      Methods  Firstly, ET0 calculated by the Penman-Monteith equation was used as the standard value. Secondly, daily meteorological data of the Yining station, in Yili Area, the Xinjiang Uygur Autonomous Region, China from 1996 to 2015 were adopted to construct 14 MARS models under different combinations of meteorological parameters and calculate ET0. The calculation results were compared with those of the generalized regression neural network (GRNN), support vector machine (SVM), and 10 empirical equations based on temperature, mass transfer, radiation, and meteorological parameters.
      Results  The calculation accuracies of MARS, GRNN, and SVM were higher than that of the empirical equations. Overall, MARS had the best performance and the highest accuracy, and SVM was slightly better than GRNN.
      Conclusion  MARS was the best model for estimating ET0 in this study.

     

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