ENVIRONMENTAL SCIENCE
Next-Gen AI for Coastal Resilience: Predicting Flood Impacts with Unprecedented Speed & Accuracy
Climate change and sea-level rise (SLR) pose escalating threats to coastal cities, intensifying the need for efficient and accurate methods to predict potential flood hazards. Traditional physics-based hydrodynamic simulators, although precise, are computationally expensive and impractical for city-scale coastal planning applications. Deep Learning (DL) techniques offer promising alternatives, however, they are often constrained by challenges such as data scarcity and high-dimensional output requirements. Leveraging a recently proposed vision-based, low-resource DL framework, we develop a novel, lightweight Convolutional Neural Network (CNN)-based model designed to predict coastal flooding under variable SLR projections and shoreline adaptation scenarios. Furthermore, we demonstrate the ability of the model to generalize across diverse geographical contexts by utilizing datasets from two distinct regions: Abu Dhabi and San Francisco. Our findings demonstrate that the proposed model significantly outperforms state-of-the-art methods, reducing the mean absolute error (MAE) in predicted flood depth maps on average by nearly 20%. These results highlight the potential of our approach to serve as a scalable and practical tool for coastal flood management, empowering decision-makers to develop effective mitigation strategies in response to the growing impacts of climate change.
Executive Impact: Key Metrics at a Glance
CASPIAN-v2, a novel deep learning model, significantly advances coastal flood prediction. It reduces Mean Absolute Error by nearly 20% compared to state-of-the-art methods, offering high-resolution flood depth maps under various sea-level rise scenarios and shoreline adaptations. The model demonstrates strong generalization across diverse geographical contexts like Abu Dhabi and San Francisco. Crucially, it transforms a months-long simulation task into a near-instantaneous prediction, drastically cutting computational time by orders of magnitude (e.g., 2,763 hours for physics-based models to under 16 seconds for a full test set). This makes CASPIAN-v2 a scalable and practical tool for real-world coastal planning, empowering decision-makers with rapid, accurate insights for effective climate adaptation strategies.
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Average MAE Reduction vs. SOTA
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Days of Simulation Time Reduced to Seconds
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Non-inundated Prediction Accuracy (Acc[0])
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Dice Similarity Coefficient (DSC)
Deep Analysis & Enterprise Applications
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Enterprise Process Flow
Hydrodynamic Simulations (Physics-Based)
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Data Preprocessing & Grid Mapping
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CASPIAN-v2 Training (Shoreline Scenarios, SLR)
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Rapid Flood Prediction (High-Resolution)
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Fine-Tuning & Adaptability (New Scenarios)
Unprecedented Computational Efficiency
~620000X Speedup over Physics-Based Simulators for 72 Scenarios (Months to Seconds)| Feature | CASPIAN-v2 | Traditional ML (Lasso w/ Poly) | Advanced DL (Swin-Unet) |
|---|---|---|---|
| MAE Reduction | 20% (vs. SOTA DL baseline) | 51.65% (vs. Lasso) | N/A |
| Spatial Accuracy (DSC) | 0.8437 (31.05% vs. Lasso) | 0.6438 | 0.8014 |
| Generalizability | Robust across AD/SF & multiple SLR (0.5m, 1.0m, 1.5m) | Limited to training domain | Requires more data for new scenarios |
| Computational Time (72 Scenarios) | < 16 seconds | Minutes | Hours |
Real-World Adaptability: Abu Dhabi & San Francisco
Dual-Region Flood Mapping: CASPIAN-v2 was rigorously validated on two distinct, vulnerable metropolitan coastal areas: Abu Dhabi and San Francisco Bay. Both regions feature low-lying topographies and significant urbanization, making them highly susceptible to SLR. The model successfully predicted coastal flooding under various SLR depths (0.5m, 1.0m, 1.5m) and diverse shoreline adaptation scenarios for both, demonstrating its robust ability to generalize across different geographical and hydrodynamic contexts.
Explainable AI & Uncertainty Quantification
To enhance trust and decision-making, CASPIAN-v2 incorporates explainable AI (Grad-CAM) to visualize model focus on vulnerable areas, and employs a deep ensemble method for predictive uncertainty quantification. This allows planners to identify high-risk zones and areas requiring further detailed hydrodynamic study, fostering a more informed and reliable approach to coastal resilience planning.
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Estimated Annual Savings
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Annual Hours Reclaimed
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Your Implementation Roadmap
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Phase 1: Data Integration & Model Setup
Establish data pipelines for ingesting bathymetry, DEM, SLR scenarios, and OLU protection configurations. Configure and initialize the CASPIAN-v2 model architecture.
Phase 2: Initial Training & Baseline Validation
Train CASPIAN-v2 on combined Abu Dhabi and San Francisco datasets. Benchmark performance against traditional and existing DL models using key metrics (AMAE, ARMSE, R2, DSC).
Phase 3: Generalization & Fine-Tuning
Evaluate model adaptability to new SLR conditions (e.g., 0.5m & 1.5m for SF) through fine-tuning using a curriculum-based strategy to prevent catastrophic forgetting. Assess performance on holdout and generalization sets.
Phase 4: Interpretability & Uncertainty Analysis
Implement Grad-CAM for visual explanations of model predictions. Utilize deep ensembles to quantify predictive uncertainty, identifying regions of higher confidence and those requiring further attention.
Phase 5: Operational Deployment & Scenario Planning
Integrate the rapid prediction capabilities of CASPIAN-v2 into a user-friendly interface for city planners. Enable rapid assessment of thousands of adaptation scenarios to inform robust coastal resilience strategies.
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