数字农科院2.0

Multi-scale nested model optimal fitting software for spatial estimation variogram of heavy metals in soil: framework, design and implementation

文献类型： 外文期刊

作者： Cao Shanshan; Sun Wei; Kong Fantao; Liu Jifang.

作者机构：

关键词： Computer software; Deep learning; Heavy metals; Interpolation; Learning systems; Python; Soils; Deep learning; Design and implementations; Framework designs; Heavy metals in soil; Multi-scales; Multiscale nested model; Optimal fitting; Spatial estimation; Spatial interpolation; Variograms; Visualization

期刊名称： 2022 IEEE International Conference on Artificial Intelligence and Computer Applications, ICAICA 2022

ISSN：

年卷期： 2022 年

页码：

收录情况： EI（2022版）

摘要： The optimal fitting of variogram is the key to study the spatial variance law of heavy metals in soil, which can effectively improve the accuracy and reliability of spatial interpolation of heavy metals in soil. In this study, the framework of multi-scale nested model optimal fitting software for heavy metals spatial estimation variogram in soil was designed by using microservice architecture. Six kinds of microservices were designed and implemented by mixed programming of Java and Python. Experiments showed that the system interface is simple and friendly, and could substantially reduce the difficulty of optimal fitting of multi-scale nested model of spatial variogram through visualization and interaction. Moreover, the optimal fitting algorithm of multiscale nested model based on deep learning could effectively improve the accuracy of spatial interpolation, and could provide a good software tool for relevant research in the field of resources and environment. © 2022 IEEE.

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