@article{SANTOSFERNANDEZ2022107446,
title = {Bayesian spatio-temporal models for stream networks},
journal = {Computational Statistics & Data Analysis},
volume = {170},
pages = {107446},
year = {2022},
issn = {0167-9473},
doi = {https://doi.org/10.1016/j.csda.2022.107446},
url = {https://www.sciencedirect.com/science/article/pii/S0167947322000263},
author = {Edgar Santos-Fernandez and Jay M. {Ver Hoef} and Erin E. Peterson and James McGree and Daniel J. Isaak and Kerrie Mengersen},
keywords = {Bayesian model, Space-time, Linear regression, Branching network, Vector autoregression},
abstract = {Spatio-temporal models are widely used in many research areas including ecology. The recent proliferation of the use of in-situ sensors in streams and rivers supports space-time water quality modelling and monitoring in near real-time. A new family of spatio-temporal models is introduced. These models incorporate spatial dependence using stream distance while temporal autocorrelation is captured using vector autoregression approaches. Several variations of these novel models are proposed using a Bayesian framework. The results show that our proposed models perform well using spatio-temporal data collected from real stream networks, particularly in terms of out-of-sample RMSPE. This is illustrated considering a case study of water temperature data in the northwestern United States.}
}


@misc{ssnbayesArxiv,
  doi = {10.48550/ARXIV.2202.07166},
  url = {https://arxiv.org/abs/2202.07166},
  author = {Santos-Fernandez, Edgar and Hoef, Jay M. Ver and McGree, James M. and Isaak, Daniel J. and Mengersen, Kerrie and Peterson, Erin E.},
  keywords = {Computation (stat.CO), Methodology (stat.ME), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {SSNbayes: An R package for Bayesian spatio-temporal modelling on stream networks},
  publisher = {arXiv},
  year = {2022},
  copyright = {Creative Commons Attribution Non Commercial No Derivatives 4.0 International}
}