A Deep Learning Framework for Comparative Analysis in Flood Detection and Area Estimation

This research presents an innovative deep learning framework designed to be utilized for area estimation and flood detection. The most suitable architecture for real-time applications is determined using this framework by a thorough comparison of advanced architectures, including U-Net, LeNet, ResNet, and VGG16. A crucial initial phase is to classify images as flooded or non-flooded using an effective Multilayer Perceptron (MLP) classifier. Users can see areas damaged by flooding and get quick assessments with the help of a simple Streamlit interface suited to foster immediate engagement. More significantly, the results show that LeNet is the most reliable model all things considered, often estimating flooded areas with the highest accuracy. Additionally, the framework delivers essential data for rapid disaster response by estimating the intensity of floods through accurate pixel analysis. The study offers helpful techniques to improve planning and response strategies for floods in an era of growing environmental uncertainty, as well as to giving important new insights into the use of deep learning in the management of floods.