Pearson correlation analysis and ANN prediction for population and temperature changes

Yue Liu; Yangchen Shen; Kaichen Zhang

doi:10.1117/12.2659718

30 November 2022 Pearson correlation analysis and ANN prediction for population and temperature changes

Yue Liu, Yangchen Shen, Kaichen Zhang

Proceedings Volume 12456, International Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP 2022); 124561B (2022) https://doi.org/10.1117/12.2659718
Event: International Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP 2022), 2022, Qingdao, China

Abstract

There are many factors that influence global temperature changes, and some methods have been proposed to find their causes and make predictions of global temperature changes. Identifying the relationship between population and temperature change and predicting the future temperature is the top priority of this study. To achieve this stated goal, this paper proposed to construct a data structure using an artificial neural network model and normalization and show the linear relationship between population size and temperature change using Pearson correlation and normalization. To make the analysis more accurate, this paper adjusted the dataset by removing useless data and eliminating the negative effects of odd sample data. The temperature data range was restricted between -1 and 1 in the normalization step of the Artificial Neural Networks model (ANN) and used the data from 1961 to 2018 as training datasets to predict the temperature in 2019. The accuracy of the ANN model, in terms of data prediction, was verified by comparing the value with the actual temperature in 2019. The world average temperature changes from 1961 to 2019 were calculated in Excel and obtained a Pearson Correlation Coefficient of 0.92727517, which indicates a strong linear relationship between the two datasets of human population and temperature change.

Citation Download Citation

Yue Liu, Yangchen Shen, and Kaichen Zhang "Pearson correlation analysis and ANN prediction for population and temperature changes", Proc. SPIE 12456, International Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP 2022), 124561B (30 November 2022); https://doi.org/10.1117/12.2659718

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