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| Title | An Internet Of Things And Ai-Powered Framework For Long-Term Flood Risk Evaluation |
|---|---|
| ID_Doc | 8598 |
| Authors | Ahmed I.; Ahmad M.; Jeon G.; Chehri A. |
| Year | 2024 |
| Published | IEEE Internet of Things Journal, 11, 3 |
| DOI | http://dx.doi.org/10.1109/JIOT.2023.3308564 |
| Abstract | Integrating Internet of Things (IoT) and artificial intelligence (AI) techniques have found widespread application in various fields, including smart cities, agriculture, and environmental monitoring. With the increasing availability of satellite imagery and other remote sensing data, deep learning algorithms can be used and trained to detect, classify, and segment flood regions in real time. In addition, deep learning techniques, such as convolutional neural networks (CNNs), have been successful in this field, enabling the automated analysis of vast amounts of satellite imagery. By combining AI-based flood detection with other data sources, such as meteorological forecasts and ground-based sensors, comprehensive flood monitoring systems that provide early warning of flood events and facilitate effective emergency response can be developed. In this article, we developed an image-based flood segmentation system called DeepLab that uses a deep learning algorithm to detect and segment the presence and extent of floods with high accuracy and speed. The neural network was trained on an extensive collection of satellite images, which were complemented by ground truth labels that indicated the presence of flooded areas. The trained DeepLabv3 model is applied to new satellite images during inference to forecast the likelihood of each pixel belonging to a flooded area. To do this, a binary flood map was generated from the pixel-level forecasts by incorporating a threshold into the output probabilities. The proposed system's accuracy was high compared to the state-of-the-art methods, as evidenced by segmentation and experimental results. The segmentation accuracy achieved an overall score of 87%. © 2014 IEEE. |
| Author Keywords | Artificial intelligence (AI); deep learning; flood risk assessment; Internet of Things (IoT); satellite imagery; sustainability |
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