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都柏林湾浊度的向量时间序列建模

Vector time series modelling of turbidity in Dublin Bay.

作者信息

Shoari Nejad Amin, McCarthy Gerard D, Kelleher Brian, Grey Anthony, Parnell Andrew

机构信息

Hamilton Institute, Insight Centre for Data Analytics, Maynooth University, Kildare, Ireland.

ICARUS,Department of Geography, Maynooth University, Maynooth, Ireland.

出版信息

J Appl Stat. 2024 Feb 11;51(14):2744-2759. doi: 10.1080/02664763.2024.2315470. eCollection 2024.

DOI:10.1080/02664763.2024.2315470
PMID:39440233
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11492412/
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.

摘要

浊度通常作为一项重要的水质指标进行监测。诸如疏浚和倾倒作业等人类活动会扰乱浊度水平，应对其进行监测和分析以评估可能产生的影响。在本文中，我们对都柏林湾浊度随空间和时间的变化进行建模，以研究倾倒和疏浚的影响，同时控制风速这一常见大气效应的影响。我们开发了一种向量自回归集成条件异方差（VARICH）方法来对不同位置和不同水深的浊度动态行为进行建模。我们使用2017 - 2018年期间的每日浊度值来拟合模型。我们表明，拟合模型的结果与观测数据一致，并且通过贝叶斯可信区间测量的不确定性得到了很好的校准。此外，我们表明，与风速相比，疏浚和倾倒对浊度产生的每日影响可忽略不计。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/909f/11492412/93fb9aea07ac/CJAS_A_2315470_F0008_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/909f/11492412/e13cf84096b0/CJAS_A_2315470_F0001_OC.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/909f/11492412/94a22705fbf7/CJAS_A_2315470_F0003_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/909f/11492412/0b5311f5e1d8/CJAS_A_2315470_F0004_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/909f/11492412/3d9fe5f6b0ce/CJAS_A_2315470_F0005_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/909f/11492412/9dfab41d8863/CJAS_A_2315470_F0006_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/909f/11492412/4a8be49d38b1/CJAS_A_2315470_F0007_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/909f/11492412/93fb9aea07ac/CJAS_A_2315470_F0008_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/909f/11492412/e13cf84096b0/CJAS_A_2315470_F0001_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/909f/11492412/bb13214c3631/CJAS_A_2315470_F0002_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/909f/11492412/94a22705fbf7/CJAS_A_2315470_F0003_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/909f/11492412/0b5311f5e1d8/CJAS_A_2315470_F0004_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/909f/11492412/3d9fe5f6b0ce/CJAS_A_2315470_F0005_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/909f/11492412/9dfab41d8863/CJAS_A_2315470_F0006_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/909f/11492412/4a8be49d38b1/CJAS_A_2315470_F0007_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/909f/11492412/93fb9aea07ac/CJAS_A_2315470_F0008_OC.jpg

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本文引用的文献

1
MultiBUGS: A Parallel Implementation of the BUGS Modelling Framework for Faster Bayesian Inference.MultiBUGS：用于更快贝叶斯推理的BUGS建模框架的并行实现。
J Stat Softw. 2020 Oct 7;95. doi: 10.18637/jss.v095.i07.
2
Continuous high-frequency monitoring of estuarine water quality as a decision support tool: a Dublin Port case study.作为决策支持工具的河口水质连续高频监测：都柏林港案例研究。
Environ Monit Assess. 2014 Sep;186(9):5561-80. doi: 10.1007/s10661-014-3803-9. Epub 2014 May 14.