ORIGINAL RESEARCH article
Front. Environ. Sci.
Sec. Environmental Informatics and Remote Sensing
Volume 13 - 2025 | doi: 10.3389/fenvs.2025.1552834
This article is part of the Research TopicNew Artificial Intelligence Methods for Remote Sensing Monitoring of Coastal Cities and EnvironmentView all 3 articles
Forecasting Sea Level Rise Using Enhanced Deep Learning Models
Provisionally accepted
- University of Dubai, Dubai, United Arab Emirates
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Climate change has significantly impacted vulnerable communities globally, with rising temperatures caused by greenhouse gas emissions accelerating global Sea Level Rise (SLR), threatening coastal infrastructure and ecosystems. This study evaluates statistical and deep learning models, including the Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) networks, for predicting SLR and visualizing potentially inundated areas in the United Arab Emirates (UAE) via an interactive web interface. Historical mean sea level (MSL) data from the National Oceanic and Atmospheric Administration (NOAA), spanning 1992 to 2024, were used for training and model evaluation. The enhanced LSTM model, integrated with a Squeeze-and-Excitation (SE) block, achieved the highest accuracy, with a Root Mean Square Error (RMSE) of 2.27, representing an improvement of 8.81% over the standalone LSTM (RMSE 2.47) and 13.66% over ARIMA (RMSE 2.58). The model forecasts sea level changes up to 2100, highlighting critical risks for low-lying coastal regions such as Umm Al Quwain, Abu Dhabi, and Dubai. A significant contribution of this study is the development of an interactive, webbased visualization platform that translates predictive models into actionable insights, assisting policymakers and urban planners in risk assessment, optimizing emergency response strategies, and implementing coastal adaptation measures. The findings underscore the value of advanced AI-driven forecasting in enhancing climate resilience and suggest future research incorporating additional environmental factors affecting MSL.
Keywords: sea level rise, LSTM, ARIMA, Attention Networks, visualization
Received: 29 Dec 2024; Accepted: 23 May 2025.
Copyright: © 2025 Zitouni, Elneel, Assad Albakri, Alkhatib and Al-Ahmad. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Mohammad Sami Zitouni, University of Dubai, Dubai, United Arab Emirates
Leena Elneel, University of Dubai, Dubai, United Arab Emirates
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