摘要
为了实现高精度的实时预报作物需水量,利用结构风险最小化准则的思想以及核函数的非线性变换把空间变换到高维寻求全局最优解的方法,以最高温度、最低温度、日平均温度、天气阴晴指数和风力等级为输入因子,建立基于支持向量回归机的参考作物腾发量实时预报模型。以江苏南京站2003至2005年的日气象资料进行模型训练和预测,并将各输入因子进行±20%增噪后建立的支持向量回归机模型与传统BP网络模型作为对照。结果表明,支持向量回归机实时预报模型不仅精度高(有效性指数87.93%,平均误差0.2609,合格率87.4%),较传统BP网络模型(有效性指数78.91%,平均误差0.3526,合格率76.8%)有更优的泛化能力,且泛化能力不会因为增噪处理而降低,有较强的适应性及参数可移植性。
A support vector regression machines model for real-time forecasting of reference evapotranspiration was developed, based on the ideas of structural risk minimization of support vector machines, as well as the global optimum capacity via the nonlinear mapping to the high-dimensional space of the kernel function. The model input included maximum, minimum and average daily temperatures, weather index and wind scale. The observation data during the period from 2003 to 2005 at Nanjing, Jiangsu Province were used as training and validating data set. A support vector regression model and an add noised support vector regression model by noise were presented, and were compared with a BP neural network model. The results showed that the proposed model had high accuracy （effectiveness index is 87.93%, the average error is 0.2609, the passing rate is 87.4%）, compared with the BP neural model （effectiveness index was 78.91%, the average error was 0.2609, the passing rate was 76.8%）, it has superior generalization capacity, the generalization ability will not reduce by noise, it has a strong adaptability and portability parameters.
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2009年第10期45-49,共5页
Transactions of the Chinese Society of Agricultural Engineering
基金
国家"863"计划项目(2006AA100202)
全国优秀博士学位论文作者专项基金项目(200546)
关键词
蒸发蒸腾量
学习算法
预报
支持向量机
增噪处理
泛化能力
实时预报
evapotranspiration
learning algorithm
forecasting
support vector regression machines
by noise
generalizability
real-time forecasting
