Faculty of Science, Agronomy Department, Hydraulics Division, Laboratory of Research in Biodiversity Interaction Ecosystem and Biotechnology, University 20 Août 1955, Route El Hadaik, BP 26, Skikda, Algeria.
Department of Hydrology and Water Management, Adam Mickiewicz University, Krygowskiego 10, 61-680, Poznań, Poland.
Environ Sci Pollut Res Int. 2022 Oct;29(47):71555-71582. doi: 10.1007/s11356-022-20953-0. Epub 2022 May 23.
Machines learning models have recently been proposed for predicting rivers water temperature (T) using only air temperature (T). The proposed models relied on a nonlinear relationship between the T and T and they have proven to be robust modelling tools. The main motivation for this study was to evaluate how the variational mode decomposition (VMD) contributed to the improvement of machines learning performances for river T modelling. Measured data collected at five stations located in Poland from 1987 to 2014 were acquired and used for the analysis. Six machines learning models were used and compared namely, K-nearest neighbor's regression (KNNR), least square support vector machine (LSSVM), generalized regression neural network (GRNN), cascade correlation artificial neural networks (CCNN), relevance vector machine (RVM), and locally weighted polynomials regression (LWPR). The six models were developed according to three scenarios. First, the models were calibrated using only the T as input and obtained results show that the models were able to predict consistently water temperature, showing a high determination coefficient (R) and Nash-Sutcliffe efficiency (NSE) with values near or above 0.910 and 0.915, respectively, and in overall the six models worked equally without clear superiority of one above another. Second, the air temperature was combined with the periodicity (i.e., day, month and year number) as input variable and a significant improvement was achieved. Both models show their ability to accurately predict river T with an overall accuracy of 0.956 for R and 0.955 for NSE values, but the LSSVM2 have some advantages such as a small errors metrics, and high fitting capabilities and it slightly surpasses the others models. Thirdly, air temperature was decomposed into several intrinsic mode functions (IMF) using the VMD method and the performances of the models were evaluated. The VMD parameters appeared to cause much influence on the prediction accuracy, exhibiting an improvement of about 40.50% and 39.12% in terms of RMSE and MAE between the first and the third scenarios, however, some models, i.e., GRNN and KNNR have not benefited from the VMD. This research has demonstrated the high capability of the VMD algorithm as a preprocessing approach in improving the accuracies of the machine learning models for river water temperature prediction.
机器学习模型最近被提出，用于仅使用空气温度 (T) 预测河流水温 (T)。所提出的模型依赖于 T 和 T 之间的非线性关系，并且已被证明是稳健的建模工具。这项研究的主要动机是评估变分模态分解 (VMD) 如何有助于提高河流 T 建模的机器学习性能。从 1987 年到 2014 年，在波兰的五个站点采集并使用了测量数据进行分析。使用了六种机器学习模型进行比较，分别是 K-最近邻回归 (KNNR)、最小二乘支持向量机 (LSSVM)、广义回归神经网络 (GRNN)、级联相关人工神经网络 (CCNN)、相关向量机 (RVM) 和局部加权多项式回归 (LWPR)。这六个模型是根据三个场景开发的。首先，仅使用 T 作为输入来校准模型，结果表明，这些模型能够一致地预测水温，表现出高决定系数 (R) 和纳什-苏特克利夫效率 (NSE)，接近或高于 0.910 和 0.915，并且总体上，六个模型的工作效果相等，没有一个明显优于其他模型。其次，将空气温度与周期性（即，天、月和年数）组合作为输入变量，取得了显著的改进。两个模型都显示出其准确预测河流 T 的能力，R 的总体精度为 0.956，NSE 的总体精度为 0.955，但 LSSVM2 具有一些优势，例如小误差指标、高拟合能力，并且略优于其他模型。第三，使用 VMD 方法将空气温度分解为几个固有模态函数 (IMF)，并评估模型的性能。VMD 参数似乎对预测精度有很大影响，在第一和第三场景之间，RMSE 和 MAE 分别提高了约 40.50%和 39.12%，然而，一些模型，即 GRNN 和 KNNR，并没有从 VMD 中受益。这项研究表明，VMD 算法作为一种预处理方法，具有提高河流水温预测的机器学习模型精度的高能力。
