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RESEARCH ON WATER TEMPERATURE PREDICTION BASED ON IMPROVED SUPPORT VECTOR REGRESSION

2021年10月27日 16:03 2022世界杯买球正规平台 点击:[]

作 者:Quan, QuanHao, ZouXifeng, HuangJingchun, Lei

作者机构:State Key Laboratory of Eco-Hydraulics in Northwest Arid RegionXi’an University of Technology Xi’an710048 ChinaShaanxi Provincial Department of Water ResourcesXi’an710004 China

出 版 物:《Neural Computing & Applications》

年 卷 期:2020年

核心收录:

中图分类:TP3[工业技术-自动化技术、计算机技术]

学科分类:08[工学]0812[工学-计算机科学与技术(可授工学、理学学位)]

基 金:This work was supported by the National Key R&D Program of China under Grant No. 2017YFC0403600 and the National Natural Science Foundation of China under Grant No. 51509202.

主 题:Predictive analyticsErrorsForecastingGenetic algorithmsMean square errorReservoirs (water)Solar radiationSupport vector machinesSupport vector regressionTemperatureVectorsEfficiency coefficientMean absolute errorMean absolute percentage errorMutual informationsRoot mean square errorsSupport vector regression (SVR)Water temperature dataWater temperatures

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. © 2020, Springer-Verlag London Ltd., part of Springer Nature.

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