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Life sciences · Preprint
arXiv · August 19, 2026
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MorphoGP is a novel machine-learning framework combining contrastive learning and Gaussian process experts to predict equilibrium beach profiles under tidal influence, evaluated on 180 profiles from Chinese tide-influenced coasts. The method reports a test RMSE of 0.297 m and 59.3% reduction versus the best baseline model, but lacks independent external validation, peer review, or assessment in other coastal regions.
Algorithm development and validation on observational coastal morphology data. Over 180 beach profiles from tide-influenced coasts along the Chinese coast; no human subjects.. Intervention: MorphoGP framework (category-specific Gaussian process model with contrastive learning and gating network). Compared with: Conventional empirical models and deep learning models. n = 180. Chinese coast (tide-influenced coastal systems).
MorphoGP reduced test RMSE by approximately 59.3% compared with the best baseline model Final test RMSE achieved: 0.297 m Framework evaluated on over 180 beach profiles from tide-influenced coasts along the Chinese coast
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A machine-learning proof-of-concept on a single regional dataset (180 Chinese beach profiles) with no clinical or translational validation, comparative effectiveness trial, or independent external validation reported.
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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 environmental conditions. However, it remains challenging due to the highly nonlinear interactions among wave, tide, and sedimentary processes. Traditional empirical and numerical models often exhibit limited adaptability across diverse coastal environments, with especially pronounced limitations in beach systems where tidal processes are important . To improve data-driven prediction under these conditions, this study proposes MorphoGP, a unified category-specific Gaussian process framework for predicting equilibrium beach profiles (EBPs) under tidal influence. The framework first introduces a ContourCluster model based on contrastive learning to classify tide-influenced beach morphologies automatically. Within each morphological category, a specialized Gaussian process expert learns statistical associations between environmental descriptors including waves, tides, and sediments and the beach profile's shape. A Gating Net then integrates the outputs of all experts through a probabilistic weighting mechanism to produce the final prediction. Evaluated on data from over 180 beach profiles from tide-influenced coasts along the Chinese coast, MorphoGP achieves improved predictive performance compared with conventional and deep learning models, reducing the test RMSE by about 59.3\% compared with the best baseline and achieving a final RMSE of 0.297 m. The proposed framework provides a physically informed, data-driven tool for equilibrium beach-profile prediction under tidal influence and coastal management, while stronger process-level physical coupling remains an important direction for future development.
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