Daily temperature prediction exploiting linear regression and LSTM-based model

Yachao Mi

doi:10.1117/12.2673698

14 April 2023 Daily temperature prediction exploiting linear regression and LSTM-based model

Yachao Mi

Proceedings Volume 12613, International Conference on Computer Vision, Application, and Algorithm (CVAA 2022); 1261315 (2023) https://doi.org/10.1117/12.2673698
Event: International Conference on Computer Vision, Application, and Algorithm (CVAA 2022), 2022, Chongqing, China

Abstract

Weather forecasting of great importance in people's daily lives as global warming occurs and more and more people receive the effects of high temperatures, with some even suffering chronic illnesses or dying as a result of the heat. In this research, Linear Regression and Neural Network (LSTM) were used to set up a model for the summer temperatures in Seoul, Korea from 2013 to 2017 and some auxiliary variables, with the average temperature, solar radiation, average relative humidity, average wind speed and average latent heat flux of the day as inputs and the average temperature of the following day as outputs, by running two models with The two models are compared and the best model is selected for weather prediction using evaluation criteria such as mean absolute error. The results showed that the mean absolute error of Linear Regression was 0.89 and that of LSTM was 0.19, and that the LSTM model generalize well than Linear Regression and no overfitting problems were observed after outputting Loss Curve. Overfitting and other related problems.

Citation Download Citation

Yachao Mi "Daily temperature prediction exploiting linear regression and LSTM-based model", Proc. SPIE 12613, International Conference on Computer Vision, Application, and Algorithm (CVAA 2022), 1261315 (14 April 2023); https://doi.org/10.1117/12.2673698

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