APPLICATION OF U-NET ARCHITECTURE FOR FLOOD DETECTION IN BAYELSA STATE - Obasi, Emmanuella C. M.; Nlerum, Promise Anebo; Eze, Francis C. (FUDMA Journal of Sciences, 2025)
Floods are recurring disasters in Bayelsa State, Nigeria, causing significant damage to infrastructure, displacement of people, and loss of livelihoods. The research aims to develop an accurate and efficient method for identifying flood-affected areas using satellite imagery and deep learning techniques. The U-Net architecture, a convolutional neural network designed for image segmentation tasks, was adapted and trained on a dataset of high-resolution satellite images including both flood and non-flood periods. The model’s performance was evaluated using various metrics, including precision, recall, and F1-score. Results demonstrate that the U-Net-based approach achieves high accuracy in delineating flood extents, outperforming traditional methods. The study also explores the model’s ability to detect flood progression over time and its potential for real-time flood monitoring. The model achieved an accuracy of 88.66%, Recall of 0.90, Loss of 0.2846, Dice of 0.90, and IoU of 0.75. This research contributes to the development of advanced flood detection systems, which can aid in disaster management and mitigation efforts in Bayelsa State and similar flood-prone regions.