Qinzhou Monthly Precipitation and Average Temperature Forecast — Based on SARIMA Model

Shing Yi Bertram Peng

doi:10.54254/2755-2721/2025.LD28427

Applied and Computational EngineeringOpen access

Qinzhou Monthly Precipitation and Average Temperature Forecast — Based on SARIMA Model

Research Article

Open Access

Qinzhou Monthly Precipitation and Average Temperature Forecast — Based on SARIMA Model

Shing Yi Bertram Peng ^1*

¹ Shanghai SMIC Private School, Shanghai, China, 201203

^*Corresponding author: peng_bertram@163.com

Published on 22 October 2025

ACE Vol.196

ISSN (Print): 2755-273X

ISSN (Online): 2755-2721

ISBN (Print): 978-1-80590-451-9

ISBN (Online): 978-1-80590-452-6

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Abstract

Qinzhou, Guangxi Zhuang Autonomous Region, China, experiences intense seasonal precipitation and relatively high temperatures, which often lead to droughts or floods. Forecasting precipitation and temperature is an essential step in taking precautions against damages caused by weather. The Seasonal Autoregressive Integrated Moving Average (SARIMA) model is effective for forecasting time series with regular patterns. This paper uses the SARIMA model to forecast the monthly precipitation and average temperature of Qinzhou. The training set comprises data provided by the National Oceanic and Atmospheric Administration (NOAA) from 2010 to 2022, inclusive, while data from 2023 to 2024 are used as the test set. By analyzing the augmented Dickey-Fuller (ADF) test results, and comparing Akaike information criterion (AIC) values and models' accuracy, sets of reasonable model parameters are selected. Coefficients of determination (R2) suggest the SARIMA model can effectively forecast monthly average temperature and precipitation, but it shows shortcomings in capturing unexpected extreme values.

Keywords:

SARIMA, Forecast, Time series, Precipitation, Temperature

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References

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References

[1]. Rui, Z., Chun-qing, G., Qiu-ju, F., & Lin-yan, P. (2012). Study on the drought and flood disasters formation mechanism in karst regions of middle Guangxi. Procedia Engineering, 28, 277-281.

[3]. Kim, W., Iizumi, T., Hosokawa, N., Tanoue, M., & Hirabayashi, Y. (2023). Flood impacts on global crop production: advances and limitations. Environmental Research Letters, 18(5), 054007.

[6]. Yadav, A., Jha, C. K., & Sharan, A. (2020). Optimizing LSTM for time series prediction in Indian stock market. Procedia Computer Science, 167, 2091-2100.

[7]. Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons.

Cite this article

Peng,S.Y.B. (2025). Qinzhou Monthly Precipitation and Average Temperature Forecast — Based on SARIMA Model. Applied and Computational Engineering,196,1-6.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-MLA 2025 Symposium: Intelligent Systems and Automation: AI Models, IoT, and Robotic Algorithms

ISBN: 978-1-80590-451-9(Print) / 978-1-80590-452-6(Online)

Editor: Hisham AbouGrad

Conference website: https://www.confmla.org/london.html

Conference date: 12 November 2025

Series: Applied and Computational Engineering

Volume number: Vol.196

ISSN: 2755-2721(Print) / 2755-273X(Online)

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