干旱区研究

干旱区研究 ›› 2012, Vol. 29 ›› Issue (3): 400-404.

• 水土资源 • 上一篇    下一篇

应用神经网络RBF估算青海省东南沙区土壤蒸发

王学全1, 刘君梅1, 杨恒华2, 赵学彬2, 陈琦1   

    1. 中国林业科学研究院 荒漠化研究所，北京 100091；
    2. 青海省治沙试验站，青海 共和县 813005
  • 收稿日期:2011-04-06 修回日期:2011-08-16 出版日期:2012-05-15 发布日期:2012-05-30
  • 作者简介:王学全（1965-），男，内蒙古呼和浩特人，副研究员，主要从事干旱区水资源研究工作.E_mail:wxq@caf.ac.cn
  • 基金资助:

    中国林科院项目(CAFYBB2011002，ZD200907)，国家自然科学基金（41130640）资助

Application of RBF Network for Calculating Desert Soil Evaporation in the QinghaiTibetan Plateau

WANG Xue-Quan1, LIU Jun-Mei1, YANG Heng-Hua2, ZHAO Xue-Bin2, CHEN Qi1   

    1.  Institute of Desertification Research, Chinese Academy of Forestry, Beijing 100091, China;
    2.  Qinghai Province Station for Desertification Control Experiment, Gonghe County 813005, Qinghai Province, China
  • Received:2011-04-06 Revised:2011-08-16 Online:2012-05-15 Published:2012-05-30

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摘要： 蒸发是地表能量平衡和水分平衡的重要组成部分。2006-2009年在青海东南部沙区半固定沙丘，利用微型蒸发器(MLS)对土壤蒸发进行了测定，结合气象观测数据，利用RBF神经网络技术，建立了沙区半固定沙丘土壤蒸发模型，并应用多元回归技术进行了验证。结果表明：已经构建的RBF神经网络计算土壤蒸发与实测值吻合较好，均方差是0.14 mm，其绝对误差和均方差均小于多元线性回归计算值。模型在确定沙区土壤蒸发中具有实用可靠的优势。

关键词: 神经网络, 土壤蒸发, 微型蒸发器, 青藏高原

Abstract: Evaporation is an important factor affecting thermal balance and water budget over the earth surface. A long-term observation of soil evaporation over semifixed dune was carried out with micro-lysimeters (MLS) in the high-frigid regions in the Qinghai-Tibetan Plateau of China during the period of 2006-2009, the dataset was consisted of the collected daily soil evaporation as the output and the corresponding meteorological observation data including relative air humidity, air temperature, wind speed and soil moisture content as the input. A desert soil evaporation model was developed to research soil evaporation over semi-fixed dune based on the radial basis function (RBF) neural network, and the multiple linear regression (MLR) was used to validate the model. The results show that the values calculated with RBF network output were consistent with the observed values, and the root mean squared error was 0.14 mm. Both the average absolute percent error and the root mean squared error for the RBF neural network were lower than those for the MLR model. The RBF neural network model is good for calculating desert soil evaporation other than the traditional mathematica1 evaporation model, and it is characterized by the simple development, high accuracy and strong adaptability.

Key words: neural network, soil evaporation, micro-lysimeter, Qinghai-Tibetan Plateau