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A Hybrid Neural Network model for Marine Dissolved Oxygen Concentrations Time-Series Forecasting Based on multi-Factor Analysis and a multi-model ensemble

引用

Engineering 2021年第12期7卷 1751-1765页

作者： Hui Liu Rui Yang Zhu Duan Haiping Wu Institute of Artificial Intelligence and Robotics(IAIR) Key Laboratory of Traffic Safety on Track of Ministry of EducationSchool of Traffic and Transportation EngineeringCentral South UniversityChangsha 410075China

Dissolved oxygen(DO)is an important indicator of aquaculture,and its accurate forecasting can effectively improve the quality of aquatic *** this paper,a new DO hybrid forecasting model is proposed that includes three stages:multi-factor analysis,adaptive decomposition,and an optimizationbased ***,considering the complex factors affecting DO,the grey relational(GR)degree method is used to screen out the environmental factors most closely related to *** consideration of multiple factors makes model fusion more ***,the series of DO,water temperature,salinity,and oxygen saturation are decomposed adaptively into sub-series by means of the empirical wavelet transform(EWT)***,five benchmark models are utilized to forecast the sub-series of EWT *** ensemble weights of these five sub-forecasting models are calculated by particle swarm optimization and gravitational search algorithm(PSOGSA).Finally,a multi-factor ensemble model for DO is obtained by weighted *** performance of the proposed model is verified by timeseries data collected by the pacific islands ocean observing system(PacIOOS)from the WQB04 station at *** evaluation indicators involved in the experiment include the Nash–Sutcliffe efficiency(NSE),Kling–Gupta efficiency(KGE),mean absolute percent error(MAPE),standard deviation of error(SDE),and coefficient of determination(R^(2)).Example analysis demonstrates that:①The proposed model can obtain excellent DO forecasting results;②the proposed model is superior to other comparison models;and③the forecasting model can be used to analyze the trend of DO and enable managers to make better management decisions.

关键词： Dissolved oxygen concentrations forecasting Time-series multi-step forecasting multi-factor analysis Empirical wavelet transform decomposition multi-model optimization ensemble

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