Abstract
In contemporary flood management, integrating advanced remote sensing and machine learning marks a significant advancement. This research presents a framework leveraging such tech for flood management. It utilizes the SEN-12 flood dataset for satellite imagery analysis, enabling precise flood mapping via pre-processing. By refining the dataset with bandwidth images, targeted flood detection becomes feasible. Convolutional Neural Networks (CNNs) are employed for correlating flood-affected areas with labelled training data, enhancing mapping accuracy due to their success in image recognition tasks. However, this research extends beyond detection to flood response strategies. Using labelled data, the framework determines safe routes through geospatial analysis. Real-time evaluation of routes’ vulnerability to flooding ensures traveller safety. This dual-purpose approach optimizes data and addresses the critical need for proactive flood mitigation measures. By integrating advanced technologies, the research aims to revolutionize flood management by providing timely detection and enhancing response strategies’ safety and efficiency. Its significance lies in potentially serving as a blueprint for future flood management initiatives. By offering real-time flood mapping insights and geospatial safety assessments, this research contributes to safeguarding lives and properties in flood-prone regions. It represents a promising shift towards comprehensive flood management systems, ensuring proactive responses to mitigate flooding’s impacts on communities and infrastructure.
Keywords: Convolutional Neural Networks, Flood Management, Geospatial Analysis, Machine Learning, Proactive Mitigation, Remote Sensing.