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.
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 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.
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.
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.
KAUST researchers have found conclusive evidence that the Red Sea completely dried out approximately 6.2 million years ago. Using seismic imaging, microfossil evidence, and geochemical dating, they determined a massive flood from the Indian Ocean refilled it in about 100,000 years. The flood carved a 320-kilometer-long submarine canyon and restored marine conditions. Why it matters: This discovery provides insights into extreme environmental events and the Red Sea's unique geological history, distinguishing it from the refilling of the Mediterranean.
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.