College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China.
Nanjing Hydraulic Research Institute, Nanjing 210029, China.
Int J Environ Res Public Health. 2022 Oct 20;19(20):13588. doi: 10.3390/ijerph192013588.
In order to fully make use of limited water resources, humans have built many water conservancy projects. The projects produce many economic benefits, but they also change the natural environment. For example, the phenomenon of water temperature stratification often occurs in deep reservoirs. Thus, effective ways are needed to predict the water temperature stratification in a reservoir to control its discharge water temperature. Empirical formula methods have low computational accuracy if few factors are considered. Mathematical model methods rely on large amounts of accurate hydrological data and cost long calculation times. The purpose of the research was to simulate water temperature stratification in a reservoir by constructing an intelligent simulation model (ISM-RWTS) with five inputs and one output, determined on the basis of artificial neural networks (ANN). A 3D numerical model (3DNM) was also constructed to provide training samples for the ISM-RWTS and be used to test its simulation effect. The ISM-RWTS was applied to the Tankeng Reservoir, located in the Zhejiang province of China, and performed well, with an average error of 0.72 °C. Additionally, the Intelligent Computation Model of Reservoir Water Temperature Stratification (ICM-RWTS) was also discussed in this paper. The results indicated that the intelligent method was a powerful tool to estimate the water temperature stratification in a deep reservoir. Finally, it was concluded that the advantages of the intelligent method lay in its simplicity of use, its lower demand for hydrological data, its well generalized performance, and its flexibility for considering different input and output parameters.
为了充分利用有限的水资源，人类修建了许多水利工程。这些工程产生了许多经济效益，但也改变了自然环境。例如，深水库中经常出现水温分层现象。因此，需要有效的方法来预测水库的水温分层，以控制其排放水的温度。如果考虑的因素较少，经验公式方法的计算精度较低。数学模型方法依赖于大量准确的水文数据，计算时间长。本研究的目的是通过构建一个具有五个输入和一个输出的智能模拟模型（ISM-RWTS）来模拟水库中的水温分层，该模型基于人工神经网络（ANN）确定。还构建了一个三维数值模型（3DNM），为 ISM-RWTS 提供训练样本，并用于测试其模拟效果。ISM-RWTS 应用于位于中国浙江省的 Tankeng 水库，效果良好，平均误差为 0.72°C。此外，本文还讨论了水库水温分层的智能计算模型（ICM-RWTS）。结果表明，智能方法是估计深水库水温分层的有力工具。最后得出结论，智能方法的优点在于其使用简单、对水文数据的要求低、泛化性能好以及能够灵活考虑不同的输入和输出参数。
