Treffer: 箱图及其Python算法在地质灾害监测数据 分析建模预处理阶段的应用研究.

In geological hazard monitoring, data analysis and modeling are of extreme importance. Through statistical analysis of monitoring data, we can gain insight into the changes of geological hazard hidden danger points and reveal the development trend. The quality of monitoring data plays a decisive role in the accuracy and reliability of data analysis modeling and disaster early warning. And data preprocessing, as a key link to improve data quality, is particularly important for the identification and processing of outliers. As a common data visualization tool, box plots are widely used in data analysis. This paper elaborates on the basic concepts and characteristic value calculation and outlier recognition cleaning algorithms based on box diagrams, and uses Python language programming to visualize them. Through practical cases, the effectiveness of box plots and their algorithms in processing geological hazard monitoring data is analyzed and verified. The results show that box plots can accurately locate and deal with outliers, and significantly improve the quality of monitoring data. [ABSTRACT FROM AUTHOR]

在地质灾害监测中, 数据分析建模是一项极为重要的工作, 通过对监测数据进行统计和分析, 可以洞察地质灾 害隐患点的变化情况, 揭示发展趋势。监测数据的质量对数据分析建模与灾害预警的准确性和可靠性起着决定性作用, 数据预处理作为提升数据质量的关键环节, 对异常值识别与处理尤为重要。箱图作为一种常见的数据可视化工具, 被广 泛用于数据分析中。文章详细阐述基于箱图的基本概念和特性值计算、异常值识别清洗算法, 用Python语言编程使之可 视化。通过实际案例, 分析验证箱图及其算法在处理地质灾害监测数据上的有效性。研究结果表明, 箱图能够精准定位 并处理异常值, 显著提高监测数据质量。 [ABSTRACT FROM AUTHOR]

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