Liu Hongbin, Xie Deti, Wu Wei
Department of Resources and Environment, University of Southwest, Chongqing 400716, China.
Environ Monit Assess. 2008 Aug;143(1-3):187-93. doi: 10.1007/s10661-007-9967-9. Epub 2007 Sep 16.
Soil water content prediction is essential to the development of advanced agriculture information systems. Because soil water content series are inherently noise and non-stationary, it is difficult to get an accurate forecasting result. Considering the problems, in this paper, a novel hybrid learning architecture is proposed according to divide-and-conquer principle, the forecasting accuracy is improved. This novel hierarchical architecture is composed of ANN (Kohonen neural network) and SVM (support vector machine). The Kohonen network is used as a classifier, which partitions the whole input space into several distinct feature regions. Then, the best SVM predictor combined with an appropriate kernel function can be achieved for correspondence regions. The experimental results based on the hybrid model exhibit good agreement with actual soil water content measurements and outperform ANN and SVM single-stage models.
土壤含水量预测对于先进农业信息系统的发展至关重要。由于土壤含水量序列本身存在噪声且非平稳，因此难以获得准确的预测结果。针对这些问题，本文根据分治原则提出了一种新颖的混合学习架构，提高了预测精度。这种新颖的分层架构由人工神经网络（Kohonen神经网络）和支持向量机（SVM）组成。Kohonen网络用作分类器，将整个输入空间划分为几个不同的特征区域。然后，针对相应区域可以实现结合适当核函数的最佳支持向量机预测器。基于混合模型的实验结果与实际土壤含水量测量结果表现出良好的一致性，并且优于人工神经网络和支持向量机单阶段模型。
