Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/80440
Title: Ensemble models with uncertainty analysis for multi-day ahead forecasting of chlorophyll a concentration in coastal waters
Authors: Shamshirband, S
Nodoushan, EJ
Adolf, JE
Manaf, AA
Mosavi, A
Chau, KW 
Keywords: Chlorophyll a
Ensemble models
Wavelet-ANN
Uncertainty analysis
Bates-Granger
Issue Date: 2019
Publisher: Hong Kong Polytechnic University, Department of Civil and Structural Engineering
Source: Engineering applications of computational fluid mechanics, 1 Jan. 2019, v. 13, no. 1, p. 91-101 How to cite?
Journal: Engineering applications of computational fluid mechanics 
Abstract: 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.
URI: http://hdl.handle.net/10397/80440
ISSN: 1994-2060
EISSN: 1997-003X
DOI: 10.1080/19942060.2018.1553742
Rights: © 2018 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis GroupThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use,distribution, and reproduction in any medium, provided the original work is properly cited.
The following publication Shamshirband, S., Nodoushan, E. J., Adolf, J. E., Manaf, A. A., Mosavi, A., & Chau, K. W. (2019). Ensemble models with uncertainty analysis for multi-day ahead forecasting of chlorophyll a concentration in coastal waters. Engineering Applications of Computational Fluid Mechanics, 13(1), 91-101 is available at https://dx.doi.org/10.1080/19942060.2018.1553742
Appears in Collections:Journal/Magazine Article

Files in This Item:
File Description SizeFormat 
Shamshirband_Ensemble_Uncertainty_Coastal.pdf2.39 MBAdobe PDFView/Open
Access
View full-text via PolyU eLinks SFX Query
Show full item record
PIRA download icon_1.1View/Download Contents

WEB OF SCIENCETM
Citations

1
Citations as of Apr 6, 2019

Page view(s)

33
Citations as of Aug 21, 2019

Download(s)

25
Citations as of Aug 21, 2019

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.