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作 者 ：邹 平,杨劲松,姚荣江.

期 刊 ：中国生态农业学报 2008年 16卷 4期 页码：835-838

关键词：土壤温度预测；时间序列；BP神经网络；滨海盐碱区；农业气象观测；

Keywords:Soil temperature forecast, Time-series, Back-Propagation neural network, Saline coastal region, Agro-meteorological observation,

摘 要 ：针对滨海盐渍区表层土壤温度时序变化复杂、高度非线性的特点，以江苏省苏北典型滩涂区域为研究对象，综合运用BP神经网络和时间序列多维拓展的方法，对长期定位监测点表土层土壤温度时间序列数据进行了分析和预测，为土壤溶质运移研究与当地作物合理布局提供理论基础和参考依据。结果表明，输入层、隐含层和输出层神经元数目分别为7、7和1的3层BP神经网络模型用于土壤温度时间序列训练仿真时效果最优，其误差平方和达最小值18.017。选定的此结构BP神经网络模型简单、实用，有良好的推广泛化能力，经独立测试样本检验，预测值与实测值的相对误差均在20%以内，平均相对误差仅为2.94%，可满足土壤温度日常预报的需要。

Abstract:Soil temperature is an important parameter in agro-meteorological observations and research on variations in soil temperature time series provides the theoretical basis for soil solute movement and proper distribution of reference crops. Owing to the complicated characteristics and strong nonlinearity of surface soil temperature time-series in saline coastal regions, a long-term data on soil temperature was analyzed and soil temperature time-series predicted by integrated Back-Propagation (BP) neural network method and expansion of multi-dimensional time-series, using experimental data from a typical coastal region in North Jiangsu Province. The results show that BP neural network model with 7 neurons of input layer, 7 neurons of hidden layer, and 1 neuron of output layer is the best for soil temperature time-series forecast, with a minimum sum squared error of 18.017. Relative errors between forecated value and measured value of the soil temperature all fall within 0～20%, with an average relative error of only 2.94%. The high range of relative error underscores the significance of BP neural network in daily soil temperature forecasting.

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