Intelligent Simulation of Water Temperature Stratification in the Reservoir

Yuan Yao, Zhenghua Gu (), Yun Li, Hao Ding and Tinghui Wang
Additional contact information
Yuan Yao: College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
Zhenghua Gu: College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
Yun Li: Nanjing Hydraulic Research Institute, Nanjing 210029, China
Hao Ding: College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
Tinghui Wang: College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China

IJERPH, 2022, vol. 19, issue 20, 1-13

Abstract: 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.

Keywords: water temperature stratification; artificial neural networks; reservoir; intelligent simulation; Tankeng hydropower station (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1660-4601/19/20/13588/pdf (application/pdf)
https://www.mdpi.com/1660-4601/19/20/13588/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:19:y:2022:i:20:p:13588-:d:947837

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().