Abu Dhabi

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Climate Adaptation-Aware Flood Prediction for Coastal Cities Using Deep Learning

arXiv · Oct 29 · Research CV

Researchers have developed a CNN-based deep learning model for predicting coastal flooding in cities under various sea-level rise scenarios. The model utilizes a vision-based, low-resource DL framework and is trained on datasets from Abu Dhabi and San Francisco. Results show a 20% reduction in mean absolute error compared to existing methods, demonstrating potential for scalable coastal flood management.

Nonlinear Traffic Prediction as a Matrix Completion Problem with Ensemble Learning

arXiv · Jan 8 · Research Traffic Management

The paper introduces a novel method for short-term, high-resolution traffic prediction, modeling it as a matrix completion problem solved via block-coordinate descent. An ensemble learning approach is used to capture periodic patterns and reduce training error. The method is validated using both simulated and real-world traffic data from Abu Dhabi, demonstrating superior performance compared to other algorithms.