Advancing flood disaster management: leveraging deep learning and remote sensing technologies

Abstract

Floods are among the most widespread and devastating natural disasters, accounting for 47% of all weather-related events and affecting over 2.3 billion people, particularly in Asia. Assessing flood-prone areas is crucial for effective disaster risk reduction, but existing flood damage estimation methods, such as depth-damage functions, often lack regional adaptability and accuracy. This study addresses this gap by integrating geospatial data, remote sensing, and artificial intelligence (AI) to identify flood-affected areas in the Kan basin, Tehran. We applied deep learning methods, specifically U-Net and fully convolutional neural network (FCN) algorithms, to optical and radar images from four flood events. Our results demonstrate that the U-Net model achieves significantly higher accuracy (88%) in identifying flood-affected areas compared to the FCN model (55% accuracy). This superior performance is further supported by the mean intersection over union (mIoU) values, with U-Net achieving 0.65, compared to 0.55 for FCN. The key message of this investigation is that deep learning, particularly the U-Net model, applied to remote sensing data holds significant promise for enhancing flood monitoring, early warning systems, and disaster management strategies by enabling more accurate and timely flood assessments.