Leveraging Machine Learning for Precipitation Prediction: Enhancing Weather Forecast Accuracy

DOI

10.2991/978-94-6463-370-2_8How to use a DOI?

Keywords

Machine learning; Precipitation Prediction; Logistic regression

Abstract

With the rapid development of science and technology, computers have been able to calculate faster than humans. For huge databases, machine learning can organize and analyze them faster than humans, and calculate them quickly through mathematical and physical models. Therefore, this study examines how machine learning can predict precipitation more accurately and efficiently in weather forecasting. In this paper, we first collected the temperature and precipitation data of Xiamen, China in the past 20 years from the National Environmental Information Center, and then studied the potential relationship between the data and how to interact with each other. We also did data visualization processing and used a logistic regression model for prediction. The final research result is a prediction of the precipitation in Xiamen, China in the next year. In the data visualization image after prediction, it can be found that the overall trend of the precipitation is similar to the historical data, but there are also differences, which proves that the prediction obtained by machine learning after analyzing a large number of data includes the historical trend and possible variables. The main research conclusion is that machine learning can make weather prediction more accurate and efficient after analyzing a large amount of data, and the use of efficient prediction models can also make weather prediction more accurate, which is beneficial to the social economy and the natural environment.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Shunyi Rao
PY  - 2024
DA  - 2024/02/14
TI  - Leveraging Machine Learning for Precipitation Prediction: Enhancing Weather Forecast Accuracy
BT  - Proceedings of the 2023 International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2023)
PB  - Atlantis Press
SP  - 67
EP  - 76
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-370-2_8
DO  - 10.2991/978-94-6463-370-2_8
ID  - Rao2024
ER  -