Data-Driven Sea Level Forecasting for Cambridge, Maryland: Leveraging Time Series Models
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V6I3P114Keywords:
Mean Sea Level, Time Series Forecasting, Neural Network Autoregression, Ocean City, MarylandAbstract
The accelerating rise in sea levels poses a significant challenge for coastal communities, necessitating accurate forecasting methods. This study evaluates the efficacy of various time series models in predicting long-term sea level changes, including ARIMA, ETS, NNETAR, THETAM, TBATS, STLM, and their hybrid combinations. Using monthly mean sea level data from Cambridge, Maryland, spanning January 1971 to February 2025, a comparative analysis was conducted. The NNAR(27,1,14)[12] model emerged as the most accurate, performing exceptionally well across all metrics, especially with very low RMSE and MAE values among all tested models. These findings underscore the potential of neural network-based approaches in sea level forecasting and highlight the importance of integrated modeling techniques as decision-support tools for local mean sea level predictions. Understanding historical sea level trends is crucial for improving future projections, and this study contributes to that knowledge base. Continued research efforts leveraging these data-driven insights can significantly enhance our ability to refine predictions and develop effective strategies to mitigate the impacts of sea level rise
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