title: [Application of simulated annealing method and neural network on optimizing soil sampling schemes based on road distribution].
abstract: Taking the soil organic matter in eastern Zhongxiang County, Hubei Province, as a research object, thirteen sample sets from different regions were arranged surrounding the road network, the spatial configuration of which was optimized by the simulated annealing approach. The topographic factors of these thirteen sample sets, including slope, plane curvature, profile curvature, topographic wetness index, stream power index and sediment transport index, were extracted by the terrain analysis. Based on the results of optimization, a multiple linear regression model with topographic factors as independent variables was built. At the same time, a multilayer perception model on the basis of neural network approach was implemented. The comparison between these two models was carried out then. The results revealed that the proposed approach was practicable in optimizing soil sampling scheme. The optimal configuration was capable of gaining soil-landscape knowledge exactly, and the accuracy of optimal configuration was better than that of original samples. This study designed a sampling configuration to study the soil attribute distribution by referring to the spatial layout of road network, historical samples, and digital elevation data, which provided an effective means as well as a theoretical basis for determining the sampling configuration and displaying spatial distribution of soil organic matter with low cost and high efficiency.
authors.forename: Zong-wei
authors.initials: ZW
authors.lastname: Han
authors.forename: Wei
authors.initials: W
authors.lastname: Huang
authors.forename: Yun
authors.initials: Y
authors.lastname: Luo
authors.forename: Chun-di
authors.initials: CD
authors.lastname: Zhang
authors.forename: Da-cheng
authors.initials: DC
authors.lastname: Qi
mesh_terms: D016014:Linear Models; D016571:Neural Networks, Computer; D012987:Soil / Q000737:chemistry*
chemical_list: D012987:Soil