Summary of Deep Vision-based Framework For Coastal Flood Prediction Under Climate Change Impacts and Shoreline Adaptations, by Areg Karapetyan et al.
Deep Vision-Based Framework for Coastal Flood Prediction Under Climate Change Impacts and Shoreline Adaptations
by Areg Karapetyan, Aaron Chung Hin Chow, Samer Madanat
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed framework trains high-fidelity Deep Vision-based coastal flood prediction models in low-data settings. This addresses the limitations of traditional physics-based hydrodynamic simulators and data-driven supervised learning methods. The framework leverages different vision models, including a fully transformer-based architecture and a Convolutional Neural Network (CNN) with additive attention gates. Additionally, a custom-designed CNN is introduced for the coastal flood prediction problem. The performance of these DL models is validated against geostatistical regression methods and traditional Machine Learning approaches, demonstrating improved prediction quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to predict coastal floods has been developed using artificial intelligence. This helps solve a big problem caused by climate change, where rising sea levels can cause flooding along coastlines. The method uses special computer models called Deep Vision-based models that are designed to work well with limited data. These models outperform traditional methods and could be used in real-world scenarios where resources are limited. |
Keywords
» Artificial intelligence » Attention » Cnn » Machine learning » Neural network » Regression » Supervised » Transformer
