基于人工神经网络的土壤颗粒组成制图

以浙江西苕溪流域为研究区，综合考虑地形和土壤类型等信息，采集典型土壤样本，测定土壤颗粒组成，并基于土壤颗粒组成与景观位置和特征之间的关系，利用径向基函数(RBF)神经网络建立了高程、坡度、平面曲率、剖面曲率、径流强度系数和地形湿度指数6个地形因子与土壤颗粒组成之间的非线性映射关系，预测土壤颗粒组成的空间分布。验证结果表明，RBF神经网络方法能够挖掘出地形因子信息与土壤颗粒组成之间的非线性映射关系，其预测精度较高，模型稳定性较好，是一种低成本、高效率的制图方法。

Mapping of soil particle composition based on artificial neural network

Taking Xitiaoxi basin, Zhejiang Province as the study area, a digital soil mapping technique was introduced in this paper based on artificial neural netweork method. The study integrated comprehensively regional topography and soil types and collected 43 soil profiles for model development and validation. Using the radial basis function (RBF) neural network, the study established a nonlinear relation between six terrain factors, i.e., elevation, slope, plan curvature, profile curvature, surface slops intensity, composite terrain index and soil particle composition and then simulated the space distribution of soil particle composition. Validation results showed that the RBF neural network method could provide a reliable spatial prediction for soil mapping. The model performance of RBF neural network as shown by prediction accuracy and stability is acceptable, which shows that the method is a mapping tool of low cost and high efficiency in the hilly area.