United Arab Emirates

This study examines the rainfall estimated by two satellite rainfall products during this event, with a detailed analysis focusing on the watershed encompassing the city of Al Ain.

This study leverages remote sensing and machine learning to classify building age and integrate this information into a comprehensive flood hazard map for Al Ain City.

This study provides insights into how privately owned groceries can complement large food storage facilities and provide sustainable public and private sectors (Public–Private Partnerships-PPP).

This seismic risk profile of the United Arab Emirates, produced by the Global Earthquake Model (GEM) Foundation summarizes key metrics of seismic risk, allowing stakeholders in risk management to get an overview of the risk in the country.

This research uses the landslide inventory of Chittagong Metropolitan Area (CMA) to create a new Artificial Intelligence (AI) based insight system for the town planners and senior disaster recovery strategists of Chittagong, Bangladesh.

The Voluntary National Report of the United Arab Emirates for the Midterm Review of the Implementation of the Sendai Framework for Disaster Risk Reduction 2015-2030 (MTR SF).

This research applies remote sensing and Geographic Information System (GIS) methods to assess the spatial distribution of natural hazards and their proximity to heritage sites in the UAE. The GIS database is available for interested users upon request.

The UN Office for Disaster Risk Reduction listed 10 reasons businesses should reduce
their disaster exposure, including risk factoring, which cannot be achieved without historical
data about hazards, their locations, magnitudes, and frequencies