Climate Change Trend Using Descriptive Time Series Technique in Machine Learning: A Case of Jimma Zone, Southwestern Ethiopia

Wendafiraw Abdisa Gemmechis

doi:doi:10.11648/j.ijema.20241203.12

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Climate Change Trend Using Descriptive Time Series Technique in Machine Learning: A Case of Jimma Zone, Southwestern Ethiopia

Wendafiraw Abdisa Gemmechis^*

Published in International Journal of Environmental Monitoring and Analysis (Volume 12, Issue 3)

Received: 25 May 2024 Accepted: 12 June 2024 Published: 2 July 2024

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Abstract

Understanding climate variability and monitoring time-series trends of temperature and rainfall is crucial for the sustainable development of our planet. This study utilized historical data from the Global Historical Climatology Network-Monthly (GHCN-M) provided by the National Centers for Environmental Information (NCEI) to analyze the temperature and rainfall data from 2015 to 2022. The analysis was conducted using Python 3.1.1 on Anaconda Jupyter Notebook and the package matplotlib 3.2.1 was used for data visualization. The results revealed a pattern of maximum rainfall between March to May for the years 2020, 2021, and 2022, while for the years 2017, 2018, and 2019, the maximum rainfall was recorded in October, December, and November. Additionally, the annual maximum rainfalls were recorded in the years 2020 and 2022, and the annual maximum temperatures for all study years were recorded in January, February, and March months. On the other hand, the annual minimum temperatures for all study years occurred in June, July, August, and September months. Similarly, annual average temperatures were recorded in January, February, and March months. This study emphasizes the importance of monitoring climate change and its impacts on our planet. By understanding climate variability and time-series trends, we can better prepare for the future and work towards a sustainable world.

Published in	International Journal of Environmental Monitoring and Analysis (Volume 12, Issue 3)
DOI	10.11648/j.ijema.20241203.12
Page(s)	48-57
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Time Series Analysis, Precipitation, Temperature, Rainfall, Jupyter Notebook, Matplotlib

References

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[3]	Ye L. M., G. X., et al., Time-series modeling and prediction of global monthly absolute temperature for environmental decision making.. Adv. Atmos. Sci.,, 2013. 30(2): p. 382-396.
[4]	Mulomba, P. M. and C. González-García, Time Series Analysis of Climatic Variables in Peninsular Spain. Trends and Forecasting Models for Data between 20th and 21st Centuries. Climate, 2021. 9(119).
[5]	Markéta, P., et al., E-TRAINEE: OPEN E-LEARNING COURSE ON TIME SERIES ANALYSIS IN REMOTE SENSING. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences,, 2023. XLVIII-1/W2-2023.
[6]	Waqas, M., et al., Potential of Artificial Intelligence-Based Techniques forRainfall Forecasting in Thailand. A Comprehensive Review. Water 2023. 15(2979).
[7]	Jones, P. D. and A. Moberg, 2003: Hemispheric and large-scale surface air temperature variations:. An extensive revision and an update to 2001. Climate, 2001. 16: p. 206-223.
[8]	Hansen, J. M., et al., Global temperature change.. Proc. National Academy of Sciences USA,, 2006. 103: p. 14288-14293.
[9]	Rahmstorf, A. S., et al., Recent climate observations compared to projections.. Science,, 2007. 316.
[10]	IPCC, Detection of climate change and attribution of causes. in Climate Change 2001: The Scientific Basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change, J. T. Houghton et al., Eds., Cambridge University Press. 2001. p. 44.
[11]	Lee, H. J. and K.-T. Sohn, Prediction of monthly mean surface air temperature in a region of China.. Adv. Atmos. Sci.,, 2007. 24: p. 503-508.
[12]	Murat, M., et al., Forecasting Daily Meteorological Time Series Using ARIMA and Regression Models. Int. Agrophys., 2018. 32.
[13]	Ebi, K. L. e. a., Association of normal weather periods and El Niño events with viral pneumonia hospitalizations in females, California 1983–1998. A. merican Journal of Public Health 91, 2001: p. 1200-1208.
[14]	Khan, S. M., Application of Deep Learning LSTM and ARIMA Models in Time Series Forecasting: A Methods Case Study analyzing Canadian and Swedish Indoor Air Pollution Data.. Austin J Med Oncol., 2022. 9(1): p. 1073.
[15]	Box, P. E. G. and M. G. Jenkins, Times Series Analysis, Forecasting and Control; Taylor & Francis on behalf of the American Statistical Association (USA). San Francisco, CA, USA, (1976).
[16]	Manzoor, A. and A. Mansaf, An Intelligent IoT-Cloud-Based Air Pollution Forecasting Model Using Univariate Time-Series Analysis.. Arabian Journal for Science and Engineering., 2023.
[17]	Hecke, T. V., Time series analysis to forecast temperature change. University College Ghent, 2000.

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APA Style

Gemmechis, W. A. (2024). Climate Change Trend Using Descriptive Time Series Technique in Machine Learning: A Case of Jimma Zone, Southwestern Ethiopia. International Journal of Environmental Monitoring and Analysis, 12(3), 48-57. https://doi.org/10.11648/j.ijema.20241203.12

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ACS Style

Gemmechis, W. A. Climate Change Trend Using Descriptive Time Series Technique in Machine Learning: A Case of Jimma Zone, Southwestern Ethiopia. Int. J. Environ. Monit. Anal. 2024, 12(3), 48-57. doi: 10.11648/j.ijema.20241203.12

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AMA Style

Gemmechis WA. Climate Change Trend Using Descriptive Time Series Technique in Machine Learning: A Case of Jimma Zone, Southwestern Ethiopia. Int J Environ Monit Anal. 2024;12(3):48-57. doi: 10.11648/j.ijema.20241203.12

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@article{10.11648/j.ijema.20241203.12,
  author = {Wendafiraw Abdisa Gemmechis},
  title = {Climate Change Trend Using Descriptive Time Series Technique in Machine Learning: A Case of Jimma Zone, Southwestern Ethiopia
},
  journal = {International Journal of Environmental Monitoring and Analysis},
  volume = {12},
  number = {3},
  pages = {48-57},
  doi = {10.11648/j.ijema.20241203.12},
  url = {https://doi.org/10.11648/j.ijema.20241203.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijema.20241203.12},
  abstract = {Understanding climate variability and monitoring time-series trends of temperature and rainfall is crucial for the sustainable development of our planet. This study utilized historical data from the Global Historical Climatology Network-Monthly (GHCN-M) provided by the National Centers for Environmental Information (NCEI) to analyze the temperature and rainfall data from 2015 to 2022. The analysis was conducted using Python 3.1.1 on Anaconda Jupyter Notebook and the package matplotlib 3.2.1 was used for data visualization. The results revealed a pattern of maximum rainfall between March to May for the years 2020, 2021, and 2022, while for the years 2017, 2018, and 2019, the maximum rainfall was recorded in October, December, and November. Additionally, the annual maximum rainfalls were recorded in the years 2020 and 2022, and the annual maximum temperatures for all study years were recorded in January, February, and March months. On the other hand, the annual minimum temperatures for all study years occurred in June, July, August, and September months. Similarly, annual average temperatures were recorded in January, February, and March months. This study emphasizes the importance of monitoring climate change and its impacts on our planet. By understanding climate variability and time-series trends, we can better prepare for the future and work towards a sustainable world.
},
 year = {2024}
}

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TY  - JOUR
T1  - Climate Change Trend Using Descriptive Time Series Technique in Machine Learning: A Case of Jimma Zone, Southwestern Ethiopia

AU  - Wendafiraw Abdisa Gemmechis
Y1  - 2024/07/02
PY  - 2024
N1  - https://doi.org/10.11648/j.ijema.20241203.12
DO  - 10.11648/j.ijema.20241203.12
T2  - International Journal of Environmental Monitoring and Analysis
JF  - International Journal of Environmental Monitoring and Analysis
JO  - International Journal of Environmental Monitoring and Analysis
SP  - 48
EP  - 57
PB  - Science Publishing Group
SN  - 2328-7667
UR  - https://doi.org/10.11648/j.ijema.20241203.12
AB  - Understanding climate variability and monitoring time-series trends of temperature and rainfall is crucial for the sustainable development of our planet. This study utilized historical data from the Global Historical Climatology Network-Monthly (GHCN-M) provided by the National Centers for Environmental Information (NCEI) to analyze the temperature and rainfall data from 2015 to 2022. The analysis was conducted using Python 3.1.1 on Anaconda Jupyter Notebook and the package matplotlib 3.2.1 was used for data visualization. The results revealed a pattern of maximum rainfall between March to May for the years 2020, 2021, and 2022, while for the years 2017, 2018, and 2019, the maximum rainfall was recorded in October, December, and November. Additionally, the annual maximum rainfalls were recorded in the years 2020 and 2022, and the annual maximum temperatures for all study years were recorded in January, February, and March months. On the other hand, the annual minimum temperatures for all study years occurred in June, July, August, and September months. Similarly, annual average temperatures were recorded in January, February, and March months. This study emphasizes the importance of monitoring climate change and its impacts on our planet. By understanding climate variability and time-series trends, we can better prepare for the future and work towards a sustainable world.

VL  - 12
IS  - 3
ER  -

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Author Information

Wendafiraw Abdisa Gemmechis

Unit of Geodesy, Geomatics’ and Gravimetry, Institute of Geophysics, Space Science and Astronomy, Addis Ababa University, Addis Ababa, Ethiopia

Contact Email

http://orcid.org/0000-0002-1616-8292

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Plain Text BibTeX RIS

APA Style

Gemmechis, W. A. (2024). Climate Change Trend Using Descriptive Time Series Technique in Machine Learning: A Case of Jimma Zone, Southwestern Ethiopia. International Journal of Environmental Monitoring and Analysis, 12(3), 48-57. https://doi.org/10.11648/j.ijema.20241203.12

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ACS Style

Gemmechis, W. A. Climate Change Trend Using Descriptive Time Series Technique in Machine Learning: A Case of Jimma Zone, Southwestern Ethiopia. Int. J. Environ. Monit. Anal. 2024, 12(3), 48-57. doi: 10.11648/j.ijema.20241203.12

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AMA Style

Gemmechis WA. Climate Change Trend Using Descriptive Time Series Technique in Machine Learning: A Case of Jimma Zone, Southwestern Ethiopia. Int J Environ Monit Anal. 2024;12(3):48-57. doi: 10.11648/j.ijema.20241203.12

Copy | Download

@article{10.11648/j.ijema.20241203.12,
  author = {Wendafiraw Abdisa Gemmechis},
  title = {Climate Change Trend Using Descriptive Time Series Technique in Machine Learning: A Case of Jimma Zone, Southwestern Ethiopia
},
  journal = {International Journal of Environmental Monitoring and Analysis},
  volume = {12},
  number = {3},
  pages = {48-57},
  doi = {10.11648/j.ijema.20241203.12},
  url = {https://doi.org/10.11648/j.ijema.20241203.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijema.20241203.12},
  abstract = {Understanding climate variability and monitoring time-series trends of temperature and rainfall is crucial for the sustainable development of our planet. This study utilized historical data from the Global Historical Climatology Network-Monthly (GHCN-M) provided by the National Centers for Environmental Information (NCEI) to analyze the temperature and rainfall data from 2015 to 2022. The analysis was conducted using Python 3.1.1 on Anaconda Jupyter Notebook and the package matplotlib 3.2.1 was used for data visualization. The results revealed a pattern of maximum rainfall between March to May for the years 2020, 2021, and 2022, while for the years 2017, 2018, and 2019, the maximum rainfall was recorded in October, December, and November. Additionally, the annual maximum rainfalls were recorded in the years 2020 and 2022, and the annual maximum temperatures for all study years were recorded in January, February, and March months. On the other hand, the annual minimum temperatures for all study years occurred in June, July, August, and September months. Similarly, annual average temperatures were recorded in January, February, and March months. This study emphasizes the importance of monitoring climate change and its impacts on our planet. By understanding climate variability and time-series trends, we can better prepare for the future and work towards a sustainable world.
},
 year = {2024}
}

Copy | Download

TY  - JOUR
T1  - Climate Change Trend Using Descriptive Time Series Technique in Machine Learning: A Case of Jimma Zone, Southwestern Ethiopia

AU  - Wendafiraw Abdisa Gemmechis
Y1  - 2024/07/02
PY  - 2024
N1  - https://doi.org/10.11648/j.ijema.20241203.12
DO  - 10.11648/j.ijema.20241203.12
T2  - International Journal of Environmental Monitoring and Analysis
JF  - International Journal of Environmental Monitoring and Analysis
JO  - International Journal of Environmental Monitoring and Analysis
SP  - 48
EP  - 57
PB  - Science Publishing Group
SN  - 2328-7667
UR  - https://doi.org/10.11648/j.ijema.20241203.12
AB  - Understanding climate variability and monitoring time-series trends of temperature and rainfall is crucial for the sustainable development of our planet. This study utilized historical data from the Global Historical Climatology Network-Monthly (GHCN-M) provided by the National Centers for Environmental Information (NCEI) to analyze the temperature and rainfall data from 2015 to 2022. The analysis was conducted using Python 3.1.1 on Anaconda Jupyter Notebook and the package matplotlib 3.2.1 was used for data visualization. The results revealed a pattern of maximum rainfall between March to May for the years 2020, 2021, and 2022, while for the years 2017, 2018, and 2019, the maximum rainfall was recorded in October, December, and November. Additionally, the annual maximum rainfalls were recorded in the years 2020 and 2022, and the annual maximum temperatures for all study years were recorded in January, February, and March months. On the other hand, the annual minimum temperatures for all study years occurred in June, July, August, and September months. Similarly, annual average temperatures were recorded in January, February, and March months. This study emphasizes the importance of monitoring climate change and its impacts on our planet. By understanding climate variability and time-series trends, we can better prepare for the future and work towards a sustainable world.

VL  - 12
IS  - 3
ER  -

Copy | Download