摘要
以归一化处理后的1986-2010年河南省农田有效灌溉面积的统计数据作为样本数据,分别采用BP神经网络和支持向量机回归两种方法建立了农田有效灌溉面积的预测模型。预测结果表明,支持向量机的预测方法具有更高的预测精度和更强的泛化能力,预测误差仅为BP神经网络预测误差的11.8%,更适合进行农田有效灌溉面积的预测。最后采用两种模型分别对河南省"十二五"期间的农田有效灌溉面积进行了预测,指出了其变化趋势。
Normalized statistical data of effective irrigated area in Henan province from 1986 to 2010 were used as samples. Prediction models based on BP neural network and support vector machine respectively was established. Predicted results showed that the method based on support vector machine had higher forecast precision and better generalization ability, fore- cast error was 11.8% of BP neural network＇s forecast error, was more suitable for predicting of effective irrigated area. Final- ly, effective irrigated area of Henan province in Chinese ＂the 12th 5-year-plan＂ was predicted respectively using two mod- els, and the trend was pointed out.
出处
《湖北农业科学》
北大核心
2013年第9期2157-2160,共4页
Hubei Agricultural Sciences
关键词
农田有效灌溉面积
BP神经网络
支持向量机
预测
effective irrigated area
BP neural network
support vector machine
prediction
