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AI Model Predicts Beach Profiles Under Tidal Influence

Xi Wu, Yanqing Wei, Hang Yin, Pengze Li, Hongshuai Qi, Xi Chen· August 20, 2026 View original

Key takeaways

  • Predicting equilibrium beach profiles under tidal influence is crucial for sustainable coastal development.
  • MorphoGP is a new AI framework that significantly improves prediction accuracy by classifying beach morphologies.
  • It uses specialized Gaussian process experts and a Gating Net for robust predictions.
  • The model outperforms conventional and deep learning methods, offering a valuable tool for coastal management.

Who benefits

Coastal EngineeringEnvironmental ManagementUrban PlanningTourismInsurance

Summary

This study introduces MorphoGP, a nonparametric Gaussian process framework for predicting equilibrium beach profiles (EBPs) under tidal influence, a critical aspect of coastal management. The model outperforms traditional and deep learning methods by classifying beach morphologies and using specialized Gaussian process experts.

This research presents MorphoGP, a novel nonparametric Gaussian process framework designed to predict equilibrium beach profiles (EBPs) in coastal areas significantly affected by tides. Accurate EBP prediction is fundamental for sustainable coastal development, informing strategies for shoreline protection and managing ecosystems amidst environmental changes. The complexity of wave, tide, and sedimentary interactions makes this a challenging task for traditional models. MorphoGP addresses these limitations by first employing a ContourCluster model, based on contrastive learning, to automatically classify different tide-influenced beach morphologies. Within each identified morphological category, a specialized Gaussian process expert is then trained to learn the statistical relationships between environmental factors (like waves, tides, and sediments) and the specific shape of the beach profile. Finally, a Gating Net probabilistically integrates the outputs from all these expert models to generate the final prediction. Evaluated on over 180 beach profiles from the Chinese coast, MorphoGP demonstrated significantly improved predictive performance, reducing the test RMSE by approximately 59.3% compared to the best baseline models. This framework offers a physically informed, data-driven tool for crucial coastal management decisions.

Why it matters

Accurate prediction of beach profiles is essential for effective coastal management, protecting infrastructure, mitigating erosion, and preserving ecosystems in the face of climate change and rising sea levels.

How to implement this in your domain

  1. 1Collect comprehensive data on coastal morphology, wave patterns, tidal cycles, and sediment characteristics.
  2. 2Collaborate with research institutions to adapt and deploy advanced AI models for specific coastal management challenges.
  3. 3Integrate predictive models into coastal engineering and environmental planning workflows.
  4. 4Train coastal managers and engineers on the use and interpretation of AI-driven morphological predictions.

Original post by Xi Wu, Yanqing Wei, Hang Yin, Pengze Li, Hongshuai Qi, Xi Chen

"arXiv:2608.18558v1 Announce Type: new Abstract: The prediction of equilibrium beach profiles under tidal influence is of fundamental importance for sustainable coastal development, informing shoreline protection strategies and managing coastal ecosystems under changing environmen…"

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Originally posted by Xi Wu, Yanqing Wei, Hang Yin, Pengze Li, Hongshuai Qi, Xi Chen on X · view source

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