An automatic identification method of marine magnetic anomalies based on the sliding window correlation coefficient method

Wang, Mingming

doi:10.1016/j.jappgeo.2022.104761

Cited by 4 publications

(1 citation statement)

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“…Generally, there are three algorithms for calculating correlation coefficient, namely Pearson correlation coefficient (PCC) method, Kendall correlation coefficient (KCC) method and Spearman correlation coefficient (SCC) method [37][38][39]. In order to further prove the scientific and accuracy of the experimental results, this paper will synthesize these three algorithms to calculate the correlation coefficient for analysis.…”

Section: Correlation Analysismentioning

confidence: 99%

Application of Correlation Analysis Based on Principal Components in the Study of Global Temperature Changes

Chen

Jiang

Cheng

et al. 2023

IJEE

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The current issue of global warming is prominent, and there are many factors that affect global temperature changes. Therefore, how to correctly judge the relationship between each influencing factor and global temperature changes and accurately find out the main reasons of global temperature rise which are the problems that must be considered and solved to alleviate global warming at present. According to previous official data, this paper proposed a correlation analysis method based on principal components to comprehensively analyze the relationship between natural disaster factors, human factors, and global temperature changes, and find out the main reasons that affect global temperature rise. Compared with traditional research methods, the new method provided in this paper can still remain scientific and accurate calculation results while reducing computational dimensions. The experimental results showed that in the relationship between natural disasters and global temperature changes, the average correlation coefficient of the principal component represented by biological disasters and geological disasters was the highest at 0.6097 and a test value of p<0.05, indicating a significant positive correlation between them and global temperature. However, the correlation coefficient of the principal component represented by floods and storms was negative, indicating a negative correlation between them and global temperature. In exploring the main factors affecting global temperature rise, both the total global population and the total global CO2 emissions had a significant positive correlation with global temperature. Among them, the average correlation coefficient of the total global population was the highest at 0.9972, and its weight also was the highest at 26.42%. Therefore, this indicates that the total global population is the most important factor affecting global temperature rise. This study can provide reference for countries to make decisions in response to global warming.

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Section: Correlation Analysismentioning

confidence: 99%