Ensemble models with uncertainty analysis for multi-day ahead forecasting of chlorophyll a concentration in coastal waters

被引:226
作者
Shamshirband, Shahaboddin [1 ,2 ]
Nodoushan, Ehsan Jafari [3 ]
Adolf, Jason E. [4 ]
Manaf, Azizah Abdul [5 ]
Mosavi, Amir [6 ,7 ,8 ]
Chau, Kwok-wing [9 ]
机构
[1] Ton Duc Thang Univ, Dept Management Sci & Technol Dev, Ho Chi Minh City, Vietnam
[2] Ton Duc Thang Univ, Fac Informat Technol, Ho Chi Minh City, Vietnam
[3] Islamic Azad Univ, Bijar Branch, Dept Civil Engn, Bijar, Iran
[4] Monmouth Univ, Dept Biol, West Long Branch, NJ USA
[5] Univ Jeddah, Fac Comp & Informat Technol, Jeddah, Saudi Arabia
[6] Bauhaus Univ Weimar, Inst Struct Mech, Weimar, Germany
[7] Obuda Univ, Inst Automat, Budapest, Hungary
[8] Inst Adv Studies Koszeg, Koszeg, Hungary
[9] Hong Kong Polytech Univ, Dept Civil & Environm Engn, Hong Kong, Peoples R China
关键词
Chlorophyll a; ensemble models; wavelet-ANN; uncertainty analysis; Bates-Granger; ARTIFICIAL-INTELLIGENCE; QUALITY PARAMETERS; RIVER; PREDICTION; NETWORKS; INDEX; BAY;
D O I
10.1080/19942060.2018.1553742
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
In this study, ensemble models using the Bates-Granger approach and least square method are developed to combine forecasts of multi-wavelet artificial neural network (ANN) models. Originally, this study is aimed to investigate the proposed models for forecasting of chlorophyll a concentration. However, the modeling procedure was repeated for water salinity forecasting to evaluate the generality of the approach. The ensemble models are employed for forecasting purposes in Hilo Bay, Hawaii. Moreover, the efficacy of the forecasting models for up to three days in advance is investigated. To predict chlorophyll a and salinity with different lead, the previous daily time series up to three lags are decomposed via different wavelet functions to be applied as input parameters of the models. Further, outputs of the different wavelet-ANN models are combined using the least square boosting ensemble and Bates-Granger techniques to achieve more accurate and more reliable forecasts. To examine the efficiency and reliability of the proposed models for different lead times, uncertainty analysis is conducted for the best single wavelet-ANN and ensemble models as well. The results indicate that accurate forecasts of water temperature and salinity up to three days ahead can be achieved using the ensemble models. Increasing the time horizon, the reliability and accuracy of the models decrease. Ensemble models are found to be superior to the best single models for both forecasting variables and for all the three lead times. The results of this study are promising with respect to multi-step forecasting of water quality parameters such as chlorophyll a and salinity, important indicators of ecosystem status in coastal and ocean regions.
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页码:91 / 101
页数:11
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