发表机构
Fudan University; Shanghai Academy of AI for Science (SAIS); East China Normal University; Third Institute of Oceanography, Ministry of Natural Resources; Xiamen University（复旦大学; 上海人工智能科学研究院; 华东师范大学; 自然资源部第三海洋研究所; 厦门大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出MorphoGP框架，通过ContourCluster模型分类潮汐海滩形态，结合高斯过程专家与门控网络预测平衡海滩剖面，在中国沿海180余个海滩数据上使测试RMSE降约59.3%，为海岸管理提供数据驱动工具。
AI 中文摘要
预测潮汐影响下的平衡海滩剖面对可持续海岸发展至关重要，可为海岸线保护策略提供依据，并在环境条件变化时管理海岸生态系统。但由于波浪、潮汐和沉积过程之间的高度非线性相互作用，这一预测仍具挑战性。传统经验模型和数值模型在不同海岸环境中的适应性有限，在潮汐过程重要的海滩系统中尤为明显。为改善此类条件下的数据驱动预测，本研究提出MorphoGP，一种用于预测潮汐影响下平衡海滩剖面（EBPs）的统一类别特定高斯过程框架。该框架首先引入基于对比学习的ContourCluster模型，自动对潮汐影响下的海滩形态进行分类；在每个形态类别内，一个专门的高斯过程专家学习包括波浪、潮汐和沉积物在内的环境描述符与海滩剖面形状之间的统计关联；随后，门控网络（Gating Net）通过概率加权机制整合所有专家的输出，生成最终预测。基于中国沿海潮汐影响海岸的180余个海滩剖面数据进行评估，MorphoGP相较于传统模型和深度学习模型的预测性能有所提升，与最佳基线相比，测试集均方根误差（RMSE）降低约59.3%，最终RMSE达到0.297米。该框架为潮汐影响下的平衡海滩剖面预测及海岸管理提供了一种结合物理信息的数据驱动工具，而更强的过程级物理耦合仍是未来发展的重要方向。
英文摘要
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.
CommentsAccepted by IEEE TGRS
