Research on water temperature prediction based on improved support vector regression

被引:115
|
作者
Quan Quan [1 ]
Zou Hao [1 ]
Huang Xifeng [2 ]
Lei Jingchun [1 ]
机构
[1] Xian Univ Technol, State Key Lab Ecohydraul Northwest Arid Reg, Xian 710048, Peoples R China
[2] Shaanxi Prov Dept Water Resources, Xian 710004, Peoples R China
来源
NEURAL COMPUTING & APPLICATIONS | 2022年 / 34卷 / 11期
基金
国家重点研发计划; 中国国家自然科学基金;
关键词
Water temperature prediction; Genetic algorithm; Support vector regression (SVR); Mutual information; Solar radiation; GA-SVR; SELECTION; LAKE; LOAD;
D O I
10.1007/s00521-020-04836-4
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper presents a model for predicting the water temperature of the reservoir incorporating with solar radiation to analyze and evaluate the water temperature of large high-altitude reservoirs in western China. Through mutual information inspection, the model shows that the dependent variable has a good correlation with water temperature, and it is added to the sample feature training model. Then, the measured water temperature data in the reservoir for many years are used to establish the support vector regression (SVR) model, and genetic algorithm (GA) is introduced to optimize the parameters, so as to construct an improved support vector machine (M-GASVR). At the same time, root-mean-square error, mean absolute error, mean absolute percentage error, and Nash-Sutcliffe efficiency coefficient are used as the criteria for evaluating the performance of SVR model, ANN model, GA-SVR model, and M-GASVR model. In addition, the M-GASVR model is used to simulate the water temperature of the reservoir under different working conditions. The results show that ANN model is the worst among the four models, while GA-SVR model is better than SVR model in terms of metric, and M-GASVR model is the best. For non-stationary sequences, the prediction model M-GASVR can well predict the vertical water temperature and water temperature structure in the reservoir area. This study provides useful insights into the prediction of vertical water temperature at different depths of reservoirs.
引用
收藏
页码:8501 / 8510
页数:10
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