Estuaries play a crucial role in the maintenance of the ecological balance of coastal ecosystems. Salinity intrusion can disrupt these fragile ecosystems, impacting aquatic life and human activities in coastal regions. An accurate prediction of salinity intrusion is essential for managing water resources and preserving ecosystems. This paper introduces a novel hybrid tool, called Hybrid-EBM model, designed to predict the salt-wedge intrusion length and the salinity at river mouth of an estuary. Combining the state-of-the-art Estuary Box Model (EBM) with machine learning algorithms, the new Hybrid-EBM model provides an accurate forecast of the salinity intrusion events. Experimental results highlight the effectiveness of Hybrid-EBM in salinity prediction with an RMSE of 3.41 psu against the 4.22 obtained by EBM. The outputs of this paper represent a significant advancement in the understanding of the impacts of salinity intrusion along the estuarine ecosystems, contributing to the sustainability of the coastal regions worldwide.

中文翻译：

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推进河口盒建模：一种新颖的混合机器学习和基于物理的方法


河口对于维持沿海生态系统的生态平衡起着至关重要的作用。盐分入侵会破坏这些脆弱的生态系统，影响沿海地区的水生生物和人类活动。准确预测盐度入侵对于管理水资源和保护生态系统至关重要。本文介绍了一种称为 Hybrid-EBM 模型的新型混合工具，旨在预测盐楔入侵长度和河口河口的盐度。新的混合 EBM 模型将最先进的河口盒模型 (EBM) 与机器学习算法相结合，可以准确预测盐分入侵事件。实验结果强调了 Hybrid-EBM 在盐度预测中的有效性，RMSE 为 3.41 psu，而 EBM 为 4.22。本文的成果代表了对河口生态系统盐分入侵影响的理解的重大进展，有助于全球沿海地区的可持续性。