Year: 2024
Type: article
Abstract: Weather changes have an important impact on human production and life as well as socio-economic activities. In recent years, people have also gradually paid attention to the study of weather, especially the study of temperature, and it is of great practical significance to find an accurate temperature prediction model. At the same time, the use of machine learning methods to construct a temperature model can predict temperature changes more accurately and quickly, and provide a more reliable basis for weather forecasting. Therefore, in response to the unsatisfactory accuracy of temperature prediction by traditional prediction models, this paper uses the time series of daily average temperature of Hangzhou City, China, from 2013 to 2022 as the sample data, and combines the traditional ARMA model with the BP neural network model, which enables the BP neural network model to better fit and predict the temperature changes. The study shows that the prediction accuracy of the BP neural network model is significantly improved compared with the ARMA model.
DOI: 10.54691/5pqmc462
Authors: Run Wen, Taotao Zheng, Yanting Ji
Institution: Zhejiang University of Science and Technology
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
SDG: Climate action
Open Access status: diamond
OpenAlex ID: W4403627443