KAUST researchers studied the meteorological origins of sea-level extremes in the Red Sea using computer simulations and the ADCIRC storm surge model. They validated their datasets with hourly sea-level observations from six tidal gauges along the Saudi coast. The study found that wind variations over the southern part of the sea are the main drivers of basin-wide sea-level extremes. Why it matters: This research provides critical insights for managing and developing the Red Sea coastline, including megacity projects and tourism, while mitigating their impact on the marine environment.
This paper introduces a deep vision-based framework for predicting coastal floods under climate change, addressing the challenges of limited training data and high-dimensional output. The framework employs and compares various deep learning models, including a custom compact CNN architecture, against geostatistical and traditional machine learning methods. A new synthetic dataset of flood inundation maps for Abu Dhabi's coast is also provided to benchmark future models.
Researchers have developed a CNN-based deep learning model for predicting coastal flooding in cities under various sea-level rise scenarios. The model utilizes a vision-based, low-resource DL framework and is trained on datasets from Abu Dhabi and San Francisco. Results show a 20% reduction in mean absolute error compared to existing methods, demonstrating potential for scalable coastal flood management.
AMRC researchers Jide Oyebanji and Tarcisio Silvia will present papers at the MATLAB User Group Meeting in Abu Dhabi. Oyebanji's paper focuses on the 'Design of an Interactive TPMS Designing Desktop App' using MATLAB's numerical capabilities. Silvia's presentation discusses the optimization of MIMO active vibration controllers for electromechanical systems using MATLAB Simulink and Particle Swarm Optimization. Why it matters: The presentations showcase the application of computational tools like MATLAB in advanced materials research and digital engineering within the UAE.
KAUST's Atmospheric and Climate Modeling group, led by Georgiy Stenchikov, is using high-resolution global and regional climate models to predict climate change in the Middle East, focusing on local atmospheric and oceanic processes. The group developed coupled regional atmospheric and oceanic models for the Red Sea, accounting for the climate effect of aerosols, especially dust, which is significant in the region. They found that dust strongly affects the Red Sea, causing high optical depth and solar cooling effect, particularly in the southern part, impacting energy balance and circulation. Why it matters: Improving regional climate models with specific attention to dust and aerosols is crucial for predicting and mitigating the environmental impacts of climate change in arid regions like the Middle East.