Forecasting Global Mean Sea Level Rise using Autoregressive Models
Authors:
- 4 December 2023
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-4
Abstract
Sea level Rise (SLR) is one of the most prominent results of climate change. This phenomenon was driven by thermal expansion of ocean water and increasing melting rates of glaciers and ice sheets, resulting in a global increase in mean sea level. SLR has resulted in significant socioeconomic and biophysical implications affecting vulnerable coastal regions and communities in both short and long terms. This study focuses on forecasting Global Mean Sea Level (GMSL) using autoregressive models, namely Autoregressive Integrated Moving Average (ARIMA) and Vector Autoregressive Process model (VAR (p)). A number of tests were conducted to forecast GMSL using historical data. Additionally, the influence of Antarctica’s mass loss on GMSL forecasting was explored. The conducted experiments show that highly accurate forecasting results of GMSL based on historical data can be achieved using ARIMA (1,0,2). Furthermore, our findings incorporating the influence of historical Antarctica mass loss data will enhance the forecasting results compared with other contributing factors to GMSL.Keywords
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