​​​FLOODINT: Monitoring Flood Events Over Arid Regions Using Remote Sensing Data and Artificial Intelligence (AI) Analysis

Abstract

Floods are extreme events that can cause severe damage to infrastructure and result in loss of life. Evidence suggests that the frequency and severity of floods are increasing globally. According to the Intergovernmental Panel on Climate Change, the frequency and intensity of extreme precipitation events are likely to increase due to climate change. Increasing temperatures can lead to more moisture in the atmosphere, which can, in turn, increase the likelihood of heavy rainfall events. In addition to climate change, other factors such as urbanization and changes in land use can also contribute to an increased risk of floods. Urbanization can lead to more impermeable surfaces, which can result in more runoff during heavy rainfall events. To mitigate the impacts of floods, it is important to improve early warning systems and disaster preparedness measures. This may involve improving flood forecasting and warning systems, implementing land-use planning and management measures to reduce the risk of flash floods, and improving infrastructure to better withstand extreme weather events. Thus, this research aims to monitor flood events in arid regions using remote sensing data and AI analysis. Our approach aims to use satellite data to detect changes in surface water and AI algorithms to analyze the data and identify flood events. As we aim at an applicative tool, we focus on the arid regions of India and Israel, where floods have significant impacts on local communities and ecosystems.​

​