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Examining sea levels forecasting using autoregressive and prophet models.

Leena Elneel1, M Sami Zitouni2, Husameldin Mukhtar2

  • 1College of Engineering and Information Technology, University of Dubai, Dubai, United Arab Emirates. lelneel@ud.ac.ae.

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|June 21, 2024
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Summary
This summary is machine-generated.

Forecasting global mean sea level (GMSL) changes is crucial. This study found autoregressive models effective for long-term sea level rise prediction, while Prophet excels at trend analysis.

Keywords:
Autoregressive modelsClimate changeProphet modelSea level rise

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Area of Science:

  • Climate Science
  • Oceanography
  • Data Science

Background:

  • Global climate change is altering sea levels worldwide.
  • Oceanic and climatic factors like temperature, ocean heat, and greenhouse gas (GHG) emissions influence sea level changes.
  • Regional sea level fluctuations, such as in the Arabian Gulf, can exceed global averages.

Purpose of the Study:

  • To evaluate time series models for forecasting Global Mean Sea Level (GMSL).
  • To investigate the influence of key climate factors on sea level rise.
  • To compare forecasting performance on global and regional sea level data.

Main Methods:

  • Applied Autoregressive Moving Average (ARIMA) and Facebook's Prophet for GMSL forecasting.
  • Utilized Vector Autoregressive (VAR) model to analyze climate drivers (ocean heat, air temperature, GHG emissions).
  • Tested models on both global sea level data and regional data from the Arabian Gulf.

Main Results:

  • Autoregressive models demonstrated strong long-term forecasting capabilities for GMSL.
  • The Prophet model effectively captured trends and patterns in sea level time series.
  • Regional data from the Arabian Gulf showed higher variability than GMSL.

Conclusions:

  • Time series models, including ARIMA and Prophet, are valuable tools for understanding and predicting sea level changes.
  • Climate factors significantly influence sea level rise, with regional variations observed.
  • Accurate sea level forecasting is essential for climate change adaptation strategies.