Prediction of global temperature based on the BP neural networks
DOI:
https://doi.org/10.54097/hset.v42i.7102Keywords:
global temperatures, Gray Prediction Method, BP Neural Network.Abstract
The problem of global warming is getting worse as global temperature has risen year by year to an alarming level. This paper is first based on historical data on global temperatures, two prediction models were developed using Gray Prediction Method and BP Neural Network model, whose results show that the root mean square error test value of BP neural network is about 0.03 lower than that of the gray prediction method (0.1627).Thus, the BP neural network prediction model is proved effective, and then the model was analyzed by gray correlation theory, which found that carbon dioxide concentration, forest cover area and human population are affecting the global temperature, and the Gray correlation of the fisrt two factors is as high as 0.8, which makes the two the main factors. In the end the paper gave measures to contain global warming.
Downloads
References
Wei Fengying, Cao Hongxing. Air temperature mutation analysis and its trend prediction study in China, Northern Hemisphere and Global [J]. Atmospheric Science, 1995 (02): 140-148.
Hou Huiqing. Global climate change prediction model based on the BP neural network [J]. Technology and Innovation, 2021(09): 10-11.DOI: 10.15913/j.cnki.kjycx. 2021.09.005.
Song Tianyang. Research on global average annual air temperature prediction based on genetic neural network [D]. North China Electric Power University, 2020.
A gray prediction model based on the BP neural network [D]. Yan Jianbo. Xi'an University of Technology, 2009
Xu Hong. Prediction of Employment Based on BP Neural Network [J]. Hunan Technology and Business University, 2020, (15).
He Jie, Hu Da, Huang Xiaolin. Deformation prediction of tunnel surrounding rock based on an improved grey theory model [J]. Hunan University of Technology, 2017,42 (05)
Gao Zhichao. Implementation carbon neutralization prediction analysis based on carbon emission data [J]. Electronic Technology and Software Engineering, 2022 (22): 201-204.
Li Junfeng, Dai Wenzhan. Research on GM (1,1) improved model and the application of power generation modeling in Shanghai [J]. System Engineering Theory and Practice, 2005, (3): 140-144.
Wang Chao. Short-term air temperature prediction study based on a deep neural network [D]. Anhui University, 2021.DOI: 10.26917/d.cnki. ganhu. 2021.002046.
Zhang Yu, Zhao Wenying, Liao Fei, Cao Shuo, Dong Ying. Comprehensive evaluation of medical development level in provinces in the Yangtze River Basin based on principal component-grey correlation analysis [J]. Scientific and technological Innovation and application, 2022,12(30): 29-32.DOI:10.19981/j.CN23-1581/G3.2022.30.007.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
