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首页> 外文期刊>Journal of Hydrology >On the complexities of sediment load modeling using integrative machine learning: Application of the great river of Loiza in Puerto Rico
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On the complexities of sediment load modeling using integrative machine learning: Application of the great river of Loiza in Puerto Rico

机译：关于沉积物载荷建模的复杂性，综合机械学习：在波多黎各的Loiza大河的应用

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摘要

Sediment transportation in water bodies may cause many problems for the water resources projects and damage the environment. Hence, modeling sediment load components, including suspended sediment load (SSL) and bedload (BL) in rivers is of prime importance. Effective modeling of SSL and BL remains a challenging task due to their complex hydrological process. On this account, this study aims to appraise the potential of conventional machine learning (ML) models including adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), and their integrative version with nature optimization algorithm called genetic algorithm (GAANFIS and GA-SVR) for SSL and BL prediction. Two traditional models are developed for modeling verification including the sediment rating curve (SRC) and multiple linear regression (MLR). The modeling results are assessed using four statistical measures (e.g., root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe Efficiency (NSE), and coefficient of determination (R-2)), diagnostic analysis (scatter plots and Taylor diagram), and evaluation of the dependence of the state of the river flow-sediment system (hysteresis analysis). Based on the attained predictability performance, the integrative ML models reveal a superior prediction capacity in comparison with the standalone ANFIS, SVR, and the traditional models. In quantitative evaluation, the proposed integrative ML models indicate a remarkable prediction enhancement approximately 44% mean magnitude based on the MAE metric against the SRC traditional model for both the SSL and BL predicted values. Overall, the current investigation evidences the potential of the nature-inspired algorithm as a hyper-parameter optimizer for ML models that produce a reliable and robust predictive model for sediment concentration quantification.
机译：在水体底泥运输可能会导致水资源的项目很多问题和破坏环境。因此，在河流沉积物建模负载组件，包括悬浮沉积物负载（SSL）和泥沙（BL）是最重要的。 SSL和BL的有效建模仍然是一个具有挑战性的任务，由于其复杂的水文过程。由于这个原因，这项研究旨在评估传统的机器学习（ML）模型，包括自适应神经模糊推理系统（ANFIS），支持向量回归（SVR），以及它们的综合版本与自然的优化算法，称为遗传算法的潜力（GAANFIS和GA-SVR）的SSL和BL预测。两个传统的模型用于模拟验证，包括泥沙等级曲线（SRC）和多元线性回归（MLR）开发。建模结果是使用四个统计度量（例如，均方根误差（RMSE），平均绝对误差（MAE），纳什萨克利夫效率（NSE），和决定系数（R-2）），诊断分析来评估（散点图和泰勒图），和河流水沙系统（滞后分析）的状态的依赖性的评价。基于所述获得可预测的性能，综合ML模型揭示在与独立ANFIS，SVR和传统模式的比较优良的预测能力。在定量评价，所提出的综合ML车型表明了显着提高预测约44％，平均幅度基础上，MAE反对的SSL和BL预测值均SRC传统模式度量。总体而言，目前的调查能证明的自然灵感的算法作为超参数优化对于产生的泥沙浓度定量的可靠和稳定的预测模型ML车型的潜力。

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