The suspended sediment load (SSL) is one of the major hydrological processes affecting the sustainability of river planning and management. Moreover, sediments have a significant impact on dam operation and reservoir capacity. To this end, reliable and applicable models are required to compute and classify the SSL in rivers. The application of machine learning models has become common to solve complex problems such as SSL modeling. The present research investigated the ability of several models to classify the SSL data. This investigation aims to explore a new version of machine learning classifiers for SSL classification at Johor River, Malaysia. Extreme gradient boosting, random forest, support vector machine, multi-layer perceptron and k-nearest neighbors classifiers have been used to classify the SSL data. The sediment values are divided into multiple discrete ranges, where each range can be considered as one category or class. This study illustrates two different scenarios related to the number of categories, which are five and 10 categories, with two time scales, daily and weekly. The performance of the proposed models was evaluated by several statistical indicators. Overall, the proposed models achieved excellent classification of the SSL data under various scenarios.

悬浮泥沙负荷（SSL）是影响河流规划和管理可持续性的主要水文过程之一。此外，沉积物对大坝运行和水库容量有显着影响。为此，需要可靠和适用的模型来计算和分类河流中的 SSL。机器学习模型的应用已成为解决 SSL 建模等复杂问题的常见方法。本研究调查了几种模型对 SSL 数据进行分类的能力。本次调查旨在探索新版本的机器学习分类器，用于马来西亚柔佛河的 SSL 分类。极端梯度提升、随机森林、支持向量机、多层感知器和k-最近邻分类器已用于对 SSL 数据进行分类。沉积物值分为多个离散范围，其中每个范围可以被视为一个类别或类别。本研究说明了与类别数量相关的两种不同情景，即 5 类和 10 个类别，具有每天和每周两个时间尺度。提出的模型的性能通过几个统计指标进行评估。总体而言，所提出的模型在各种场景下都实现了对 SSL 数据的出色分类。