Deep learning-enhanced satellite-based Flood detection for early Warning and prevention

Abstract

The integration of deep learning techniques into satellite-based flood detection systems aims to improve early warning and prevention measures. Flooding poses significant risks to human lives and infrastructure, necessitating effective monitoring and mitigation strategies. This project leverages deep learning techniques, specifically transfer learning, to enhance satellite-based flood detection systems. By utilizing pre-trained models such as ResNet-50, VGG-16, and MobileNet-V2, we analyze satellite imagery to detect and map flood-prone areas with high precision. CNNs have the ability to can learn complex patterns, which significantly improves detection accuracy automatically. Our approach demonstrated that custom CNN architectures could achieve superior accuracy, with our custom model reaching 96.7% accuracy. Transfer learning also proved effective, with models like VGG-16 and MobileNet-V2 achieving 95% accuracy. However, the hybrid model showed a high accuracy of 98% with the pre-trained DenseNet201 model, demonstrating its efficacy for classification. Applying quantum computing techniques in flood detection systems could offer substantial advantages, including enhanced computational power and speed. These advancements could lead to more accurate and efficient future flood prediction and response strategies.