The prediction and analysis of global climate change based on SARIMA

Research Article
Open access

The prediction and analysis of global climate change based on SARIMA

Dongyao Liu 1*
  • 1 Jinan Foreign Language School    
  • *corresponding author 1811000211@mail.sit.edu.cn
Published on 21 February 2024 | https://doi.org/10.54254/2755-2721/40/20230665
ACE Vol.40
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-305-0
ISBN (Online): 978-1-83558-306-7

Abstract

Global climate change is a significant challenge that the world is currently facing. Accurate prediction of global climate change is essential for environmental protection, agricultural production, and social development. This study explores the utilization of the Seasonal Autoregressive Integrated Moving Average (SARIMA) model for forecasting global climate change. The SARIMA model is a machine learning algorithm that can effectively capture seasonal patterns and non-linear characteristics of climate data. The study initiates by performing data preprocessing tasks, which encompass data cleaning, managing missing values, and converting the data into a suitable format for analysis. The SARIMA model is then constructed, considering the seasonality and autocorrelation of the climate data. Historical climate data is used to train the SARIMA models, which are then utilized to forecast future global climate changes. The predictive performance of the models is evaluated to validate the effectiveness and accuracy of the SARIMA model in global climate change prediction. Experimental results indicate that the SARIMA model effectively captures the underlying patterns and dynamics of the climate data. The accurate predictions of the SARIMA model have practical implications for understanding and forecasting global climate change. These forecasts provide insightful information for policy formulation and decision-making, aiding in the development of innovative strategies to mitigate and adapt to climate change.

Keywords:

Global Climate Change, SARIMA Model, Prediction, Data Preprocessing, Seasonality

Liu,D. (2024). The prediction and analysis of global climate change based on SARIMA . Applied and Computational Engineering,40,268-273.
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References

[1]. Abbass K Qasim M Z Song H et al 2022 A review of the global climate change impact adaptation and sustainable mitigation measures Environmental Science and Pollution Research 29(28): pp 42539-42559

[2]. Kang Y Khan S Ma X 2009 Climate change impacts on crop yield crop water productivity and food security–A review Progress in Natural Science 19(12): pp 1665-1674

[3]. Shad M Sharma YD & Singh A 2022 Forecasting of monthly relative humidity in Delhi India using SARIMA and ANN models Model Earth Syst Environ 8: pp 4843–4851

[4]. Yerlikaya B A Ömezli S Aydoğan N 2020 Climate change forecasting and modeling for the year 2050 Environment climate plant and vegetation growth pp 109-122

[5]. Parasyris A Alexandrakis G Kozyrakis G V et al 2022 Predicting meteorological variables on local level with SARIMA LSTM and hybrid techniques Atmosphere 13(6): pp 878

[6]. Valipour M 2015 Long‐term runoff study using SARIMA and ARIMA models in the United States Meteorological Applications 22(3): pp 592-598

[7]. Manigandan P Alam M D S Alharthi M et al 2021 Forecasting natural gas production and consumption in United States-Evidence from SARIMA and SARIMAX models Energies 14(19): p 6021

[8]. XHABAFTI M SINAJ V 2022 Weather forecasting based on the application of SARIMA models CIRCULAR ECONOMY p 549

[9]. Zia S 2021 Climate Change Forecasting Using Machine Learning SARIMA Model iRASD Journal of Computer Science and Information Technology 2(1): pp 01-12

[10]. Dataset https://www.kaggle.com/datasets/berkeleyearth/climate-change-earth-surface-temperature-data

Cite this article

Liu,D. (2024). The prediction and analysis of global climate change based on SARIMA . Applied and Computational Engineering,40,268-273.

Data availability

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

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About volume

Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation

ISBN：978-1-83558-305-0(Print) / 978-1-83558-306-7(Online)
Editor：Mustafa İSTANBULLU
Conference website: https://2023.confmla.org/
Conference date: 18 October 2023
Series: Applied and Computational Engineering
Volume number: Vol.40
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Abbass K Qasim M Z Song H et al 2022 A review of the global climate change impact adaptation and sustainable mitigation measures Environmental Science and Pollution Research 29(28): pp 42539-42559

[2]. Kang Y Khan S Ma X 2009 Climate change impacts on crop yield crop water productivity and food security–A review Progress in Natural Science 19(12): pp 1665-1674

[3]. Shad M Sharma YD & Singh A 2022 Forecasting of monthly relative humidity in Delhi India using SARIMA and ANN models Model Earth Syst Environ 8: pp 4843–4851

[4]. Yerlikaya B A Ömezli S Aydoğan N 2020 Climate change forecasting and modeling for the year 2050 Environment climate plant and vegetation growth pp 109-122

[5]. Parasyris A Alexandrakis G Kozyrakis G V et al 2022 Predicting meteorological variables on local level with SARIMA LSTM and hybrid techniques Atmosphere 13(6): pp 878

[6]. Valipour M 2015 Long‐term runoff study using SARIMA and ARIMA models in the United States Meteorological Applications 22(3): pp 592-598

[7]. Manigandan P Alam M D S Alharthi M et al 2021 Forecasting natural gas production and consumption in United States-Evidence from SARIMA and SARIMAX models Energies 14(19): p 6021

[8]. XHABAFTI M SINAJ V 2022 Weather forecasting based on the application of SARIMA models CIRCULAR ECONOMY p 549

[9]. Zia S 2021 Climate Change Forecasting Using Machine Learning SARIMA Model iRASD Journal of Computer Science and Information Technology 2(1): pp 01-12

[10]. Dataset https://www.kaggle.com/datasets/berkeleyearth/climate-change-earth-surface-temperature-data

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