摘要
基于随机过程,将中稻平均单产分离为趋势产量和气候产量,联合运用灰色系统GM(1,1)模型、多元线性回归、自回归模型建立了漳河灌区中稻平均单产预测组合模型。该模型在率定期(1988—2009年)和验证期(2010—2013年)的相对误差均为6%左右,模型Nash-Sutcliffe效率系数为0.65。其预测结果与传统的直接模拟相比,预测精度有很大提高,与BP神经网络模型的预测结果基本一致,具有可靠的精度,考虑到该组合模型能给出具体的表达式且预测结果更为稳定,故更利于实际应用。
Based on stochastic process, the average middle-season rice yield could be divided into trend yields and climate yields. The middle-season rice prediction model in Zhanghe irrigation area was established with the joint use of gray GM（1,1） model, multiple linear regression and autoregression. The average relative errors were nearly 6 % in both the calibration and validation stages, and the Nash-Sutcliffe efficiency was 0.65. The predicting result was consistent with that of BP neural network model. However, this combinatorial model could give a certain formula and the prediction process would be more stable. Thus, the established combinatorial predicting model was reliable and could be applied in the practice.
出处
《灌溉排水学报》
CSCD
北大核心
2014年第4期356-359,共4页
Journal of Irrigation and Drainage
基金
高等学校博士学科点专项科研基金项目(20130141110014)
关键词
随机过程
中稻
平均单产
预测模型
stochastic process
middle-season rice
average yields
predicting model
