Effectively assessing crucial monitoring sites with suspended sediment concentration (SSC) is a vital challenge for achieving accurate prediction of sediment flux on sluice gates at a dam in a reservoir watershed. To address this issue, an assessment framework based on a core concept of Data-Information-Knowledge-Wisdom (DIKW) hierarchy is proposed in this study. First, for the reasonable training of the coupled method, a two-dimentional layer-averaged density current model, SRH2D, is applied to simulate reasonable SSC data. The limited SSC data at monitoring sites collected from the field and at dam face, inflow, and outflow discharges are collected for validation of a calibrated numerical model. Second, a well-known data-driven method, Support Vector Machine (SVM), is coupled with Multi-Objective Genetic Algorithm (MOGA) as a sediment-flux-prediction (SFP) model in the proposed framework to evaluate effective monitoring sites with SSC. An application in the Shih-Men Reservoir is implemented to demonstrate the contribution of the proposed investigation framework. The results indicate that the spatial turbidity current movement is reasonably simulated by the numerical model and appropriate as reliable data for the SFP model. The SSCs at measured points located on the lower level at dam face are significantly higher. Moreover, the results also show that the simulated SSC at the monitoring sites located near the inflow point and dam face are relatively useful for SFP. The analyzed results are concluded that the well-established observation equipment at the inflow point and near the dam is necessary for obtaining high-quality measured data, which has become a significant key issue on reservoir operation management (ROM). Also, the proposed framework is expected to be helpful to improve the benefit of ROM as reference for decision makers.

有效地评估具有悬浮沉积物浓度（SSC）的关键监测点，对于准确预测水库集水区大坝闸门上的泥沙通量是一项至关重要的挑战。为了解决这个问题，本研究提出了一种基于数据-信息-知识-智慧（DIKW）层次结构的核心概念的评估框架。首先，为了对耦合方法进行合理训练，将二维层平均密度电流模型SRH2D应用于模拟合理的SSC数据。在现场收集的监测点以及坝面，流入和流出流量的有限SSC数据将被收集，以验证校准的数值模型。其次，一种众所周知的数据驱动方法，即支持向量机（SVM），结合多目标遗传算法（MOGA）作为拟议框架中的泥沙通量预测（SFP）模型，以评估SSC的有效监测点。Shih-Men水库中的一项应用得以实现，以证明拟议调查框架的贡献。结果表明，数值模型合理地模拟了空间浊流的运动，并可以作为SFP模型的可靠数据。位于坝面较低水平面的测量点的SSC明显较高。此外，结果还表明，位于流入点和坝面附近的监测点处的模拟SSC对于SFP相对有用。分析结果表明，在流入点和大坝附近建立完善的观测设备对于获得高质量的测量数据是必不可少的，这已成为水库运行管理（ROM）的重要关键问题。此外，所提议的框架有望有助于提高ROM的利益，为决策者提供参考。