主成分回归模型在农业需水量预测中的应用

Agricultural Water Demand Forecast Based on Principal Component Regression Model

农业需水量的准确预测对区域发展具有十分重要的意义。农业需水量受多重因素的影响,且这些因素大多存在较强的相关性。通过介绍主成分分析法的原理和计算分析,以实例(Z市1998—2010年农业用水资料)建立回归模型对需水量进行预测。结果表明,该模型应用于农业用水量预测,其结果与当地实际情况较吻合,模型的拟合程度和预测准确度均较好。

The accurate prediction of the agricultural water demand had great significance for the development of the city. Considering that the agricultural water demand was affected by multiple factors, and most of these factors had strong relevance, based on the principle and calculation steps of the principal component analysis, and introducing an instance （agricultural water consumption data of Z city during 1998 - 2010）, were gression model to predict the water demand was established. The results showed that the model was applied to forecasting agricultural water demand, and the conclusion agrees was quite well with the local actual conditions, the model fitting degree and prediction accuracy were ideal.