Modeling Sea Level Rise Using Ensemble Techniques: Impacts on Coastal Adaptation, Freshwater Ecosystems, Agriculture and Infrastructure

Autor: Sambandh Bhusan Dhal, Rishabh Singh, Tushar Pandey, Sheelabhadra Dey, Stavros Kalafatis, Vivekvardhan Kesireddy
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Analytics, Vol 3, Iss 3, Pp 276-296 (2024)
Druh dokumentu: article
ISSN: 2813-2203
DOI: 10.3390/analytics3030016
Popis: Sea level rise (SLR) is a crucial indicator of climate change, primarily driven by greenhouse gas emissions and the subsequent increase in global temperatures. The impact of SLR, however, varies regionally due to factors such as ocean bathymetry, resulting in distinct shifts across different areas compared to the global average. Understanding the complex factors influencing SLR across diverse spatial scales, along with the associated uncertainties, is essential. This study focuses on the East Coast of the United States and Gulf of Mexico, utilizing historical SLR data from 1993 to 2023. To forecast SLR trends from 2024 to 2103, a weighted ensemble model comprising SARIMAX, LSTM, and exponential smoothing models was employed. Additionally, using historical greenhouse gas data, an ensemble of LSTM models was used to predict real-time SLR values, achieving a testing loss of 0.005. Furthermore, conductance and dissolved oxygen (DO) values were assessed for the entire forecasting period, leveraging forecasted SLR trends to evaluate the impacts on marine life, agriculture, and infrastructure.
Databáze: Directory of Open Access Journals