Abstract：
The sediment transport, involving the movement of the bedload and suspended sediment in the basins, is a critical environmental concern that worsens water scarcity and leads to degradation of land and its ecosystems. Machine learning (ML) algorithms have emerged as powerful tools for predicting sediment yield. However, their use by decision-makers can be attributed to concerns regarding their consistency with the involved physical processes. In light of this issue, this study aims to develop a physics-informed ML approach for predicting sediment yield. To achieve this objective, Gaussian, Center, Regular, and Direct Copulas were employed to generate virtual combinations of physical of the sub-basins and hydrological datasets. These datasets were then utilized to train deep neural network (DNN), conventional neural network (CNN), Extra Tree, and XGBoost (XGB) models. The performance of these models was compared with the modified universal soil loss equation (MUSLE), which serves as a process-based model. The results demonstrated that the ML models outperformed the MUSLE model, exhibiting improvements in Nash-Sutcliffe efficiency (NSE) of approximately 10%, 18%, 32%, and 41% for the DNN, CNN, Extra Tree, and XGB models, respectively. Furthermore, through Sobol sensitivity and Shapley additive explanation-based interpretability analyses, it was revealed that the Extra Tree model displayed greater consistency with the physical processes underlying sediment transport as modeled by MUSLE. The proposed framework provides new insights into enhancing the accuracy and applicability of ML models in forecasting sediment yield while maintaining consistency with natural processes. Consequently, it can prove valuable in simulating process-related strategies aimed at mitigating sediment transport at watershed scales, such as the implementation of best management practices.
摘要：
泥沙输送,涉及盆地中床负荷和悬浮泥沙的移动，是一个严重的环境问题，加剧了水资源短缺，导致土地及其生态系统退化。机器学习（ML）算法已成为预测沉积物产量的强大工具。然而,决策者使用它们可以归因于对它们与所涉及的物理过程的一致性的担忧。鉴于这个问题,这项研究旨在开发一种基于物理学的ML方法来预测沉积物产量。为了实现这一目标,高斯,Center,常规,和直接Copulas被用来生成子盆地和水文数据集的物理虚拟组合。然后利用这些数据集来训练深度神经网络(DNN)，传统神经网络(CNN)，额外的树，和XGBoost(XGB)模型。将这些模型的性能与修正的通用土壤流失方程(MUSLE)进行了比较，它作为基于流程的模型。结果表明，ML模型优于MUSLE模型，纳什-萨克利夫效率(NSE)提高了约10%，18%,32%,DNN占41%，CNN,额外的树，和XGB型号，分别。此外，通过Sobol灵敏度和基于Shapley加性解释的可解释性分析，结果表明，ExtraTree模型与MUSLE建模的沉积物运输基础物理过程具有更大的一致性。拟议的框架为提高ML模型在预测沉积物产量方面的准确性和适用性提供了新的见解，同时保持与自然过程的一致性。因此,它可以证明在模拟过程相关的策略是有价值的，旨在减轻流域尺度的泥沙运输，例如实施最佳管理实践。
