A KAUST report, in collaboration with AEON Collective and KAPSARC, warned of increasing flash floods in the Arabian Peninsula due to climate change. The report predicts a 33% increase in annual maximum rainfall by the end of the century under a high emissions scenario. KAUST is supporting MEWA to improve dam management and flash flood warning systems, leveraging its data and supercomputing capabilities. Why it matters: The study highlights the urgent need for infrastructure adaptation and improved warning systems in the region to mitigate the increasing risk of climate-related disasters.
KAUST researchers have developed a dual-use wireless sensor system that monitors both traffic congestion and flood incidents in cities. The system combines ultrasonic range finders and infrared thermal sensors to provide real-time, accurate data on traffic flow and roadway flooding. Data is sent to central servers and assimilated with satellite data to form real-time maps and forecasts. Why it matters: This technology can provide up-to-the-minute warnings for flash floods and traffic, enabling rapid emergency response and potentially saving lives in urban environments.
KAUST scientists are developing models to predict extreme weather events like the 2009 Jeddah flood, which caused significant damage. Prof. Ibrahim Hoteit's team is using data from satellites, international sources, and local entities like PME and the Jeddah Municipality to build high-resolution models. The aim is to improve predictions of extreme rain events by one or two days and issue timely warnings. Why it matters: Improving extreme weather prediction is crucial for mitigating the impact of climate change in vulnerable regions like the GCC.
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.
Sadeem, a startup founded by KAUST Ph.D. graduates, develops flood and traffic sensors powered by solar batteries and transceivers. The company's technology originated as a Ph.D. project and has been supported by the KAUST Entrepreneurship Center, including participation in the KAUST Hikma IP-based Startup Accelerator program. Sadeem's sensors are designed to mitigate damage and save lives from floods, with ten nodes currently operating on the KAUST campus. Why it matters: The development and deployment of such sensor technologies in Saudi Arabia could improve urban resilience and disaster response in flood-prone areas across the GCC region.
MBZUAI researchers are developing an AI-powered tool for flood assessment using satellite data and computer vision, prompted by the recent extreme weather in the Gulf region. The prototype analyzes spatial satellite imagery from before and after the storm to detect changes and identify heavily impacted roads and critical infrastructure. The tool uses AI models, Sentinel-2 imagery, and OpenStreetMap data to locate affected areas and estimate water depth. Why it matters: This research offers a way to automate and improve rapid response to extreme weather events, providing local authorities with critical information for rescue, recovery, and future urban planning in the face of climate change.
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.