State Key Laboratory of Hydrology-Water resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing, 210029, China.
Faculty of Science, Agronomy Department, Hydraulics Division, University 20 Août 1955, Route El Hadaik, BP 26, Skikda, Algeria.
Environ Sci Pollut Res Int. 2019 Jan;26(1):402-420. doi: 10.1007/s11356-018-3650-2. Epub 2018 Nov 7.
River water temperature is a key control of many physical and bio-chemical processes in river systems, which theoretically depends on multiple factors. Here, four different machine learning models, including multilayer perceptron neural network models (MLPNN), adaptive neuro-fuzzy inference systems (ANFIS) with fuzzy c-mean clustering algorithm (ANFIS_FC), ANFIS with grid partition method (ANFIS_GP), and ANFIS with subtractive clustering method (ANFIS_SC), were implemented to simulate daily river water temperature, using air temperature (T), river flow discharge (Q), and the components of the Gregorian calendar (CGC) as predictors. The proposed models were tested in various river systems characterized by different hydrological conditions. Results showed that including the three inputs as predictors (T, Q, and the CGC) yielded the best accuracy among all the developed models. In particular, model performance improved considerably compared to the case where only T is used as predictor, which is the typical approach of most of previous machine learning applications. Additionally, it was found that Q played a relevant role mainly in snow-fed and regulated rivers with higher-altitude hydropower reservoirs, while it improved to a lower extent model performance in lowland rivers. In the validation phase, the MLPNN model was generally the one providing the highest performances, although in some river stations ANFIS_FC and ANFIS_GP were slightly more accurate. Overall, the results indicated that the machine learning models developed in this study can be effectively used for river water temperature simulation.
河水温度是河流系统中许多物理和生化过程的关键控制因素，其理论上取决于多种因素。在这里，我们实现了四种不同的机器学习模型，包括多层感知机神经网络模型（MLPNN）、具有模糊 C-均值聚类算法的自适应神经模糊推理系统（ANFIS_FC）、具有网格分区方法的自适应神经模糊推理系统（ANFIS_GP）和具有消减聚类方法的自适应神经模糊推理系统（ANFIS_SC），这些模型用于模拟日河水温度，使用空气温度（T）、河流水流量（Q）和公历（CGC）的组成部分作为预测因子。所提出的模型在具有不同水文条件的各种河流系统中进行了测试。结果表明，将三个输入（T、Q 和 CGC）作为预测因子，在所有开发的模型中具有最佳的准确性。特别是，与仅使用 T 作为预测因子的情况相比，模型性能得到了显著提高，而在低地河流中，Q 的作用相对较小。在验证阶段，MLPNN 模型通常提供最高的性能，尽管在一些河流站，ANFIS_FC 和 ANFIS_GP 稍微更准确。总体而言，结果表明，本研究开发的机器学习模型可有效地用于河水温度模拟。
