Sirola Miki, Koskinen Markku, Polvinen Tatu, Pihlatie Mari
Environmental Soil Science, Department of Agricultural Sciences, Faculty of Agriculture and Forestry, Institute of Atmospheric and Earth System Research, University of Helsinki, P.O. Box 56, FI-00014 Helsinki, Finland.
Sensors (Basel). 2024 May 29;24(11):3507. doi: 10.3390/s24113507.
Exploring data aids in the comprehension of the dataset and the system's essence. Various approaches exist for managing numerous sensors. This study perceives operational states to clarify the physical dynamics within a soil environment. Utilizing Principal Component Analysis (PCA) enables dimensionality reduction, offering an alternative perspective on the spring soil dataset. The K-means algorithm clusters data densities, forming the groundwork for an operational state description. Soil data, integral to an ecosystem, entails evident attributes. Employing dynamic visualization, including animations, constitutes a vital exploration angle. Greenhouse gas variables have been added to PCA to achieve more understanding in the interconnection of gas exchange and soil properties. Pit data and flux data are analysed both separately and together using a data-driven approach. The results look promising, showing the potential to add new values and more detailed state structures to ecological models. All experiments are conducted within the Jupyter programming environment, utilizing Python 3. The relevant literature on data visualization is examined. Through combined techniques and tools, the potential features of the soil ecosystem are observed and identified.
探索数据有助于理解数据集和系统的本质。存在多种管理众多传感器的方法。本研究通过感知运行状态来阐明土壤环境中的物理动态。利用主成分分析（PCA）可实现降维，为春季土壤数据集提供了另一种视角。K均值算法对数据密度进行聚类，构成了运行状态描述的基础。土壤数据是生态系统的重要组成部分，具有明显的属性。采用包括动画在内的动态可视化是一个重要的探索角度。已将温室气体变量添加到主成分分析中，以更深入了解气体交换与土壤特性之间的相互联系。采用数据驱动的方法分别和综合分析坑数据和通量数据。结果看起来很有前景，显示出为生态模型增添新价值和更详细状态结构的潜力。所有实验均在Jupyter编程环境中使用Python 3进行。查阅了有关数据可视化的相关文献。通过组合技术和工具，观察并识别了土壤生态系统的潜在特征。
