Shoari Nejad, Amin and McCarthy, Gerard D. and Kelleher, Brian and Grey, Anthony and Parnell, Andrew (2024) Vector time series modelling of turbidity in Dublin Bay. Journal of Applied Statistics. pp. 1-16. ISSN 0266-4763

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Abstract

Turbidity is commonly monitored as an important water quality index. Human activities, such as dredging and dumping operations,can disrupt turbidity levels and should be monitored and analysed for possible effects. In this paper, we model the variations of turbidity in Dublin Bay over space and time to investigate the effects of dumping and dredging while controlling for the effect of wind speed as a common atmospheric effect. We develop a Vector Auto-Regressive Integrated Conditional Heteroskedasticity (VARICH)approach to modelling the dynamical behaviour of turbidity over different locations and at different water depths. We use daily values of turbidity during the years 2017–2018 to fit the model. We show that the results of our fitted model are in line with the observed data and that the uncertainties, measured through Bayesian credible intervals,are well calibrated. Furthermore, we show that the daily effects of dredging and dumping on turbidity are negligible in comparison to that of wind speed

Item Type:	Article
Additional Information:	This work was supported by the Science Foundation Ireland (SFI) Investigator [award number16/IA/4520]. In addition, Andrew Parnell’s work was supported by the Science Foundation Ire-land Career Development [award number 17/CDA/4695]; a Marine Research Programme funded by the Irish Government, co-financed by the European Regional Development Fund [grant-aid agreement number PBA/CC/18/01]; European Union’s Horizon 2020 Research and Innovation Programme InnoVar [grant agreement number 818144]; SFI Centre for Research Training in Foundations of Data Science [grant number 18/CRT/6049], and SFI Research Centre [award number12/RC/2289_P2]. For the purpose of Open Access, the authors have applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.
Keywords:	Bayesian; vector; autoregression; turbidity;
Academic Unit:	Faculty of Science and Engineering > Research Institutes > Hamilton Institute Faculty of Social Sciences > Research Institutes > Irish Climate Analysis and Research Units, ICARUS
Item ID:	18205
Identification Number:	https://doi.org/10.1080/02664763.2024.2315470
Depositing User:	Corinne Voces
Date Deposited:	27 Feb 2024 09:13
Journal or Publication Title:	Journal of Applied Statistics
Publisher:	Taylor & Francis
Refereed:	Yes
Funders:	Science Foundation Ireland (SFI) Investigator [award number16/IA/4520
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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