The authors gratefully acknowledge those people who have contributed their knowledge and time to the development of GWSDAT.
The authors wish to express their gratitude to Adrian Bowman, Ludger Evers and Daniel Molinari from the department of Statistics, University of Glasgow, for their invaluable contributions to the development of the spatiotemporal algorithm.
Thanks also to Ewan Crawford from the University of Glasgow for his assistance in the development of the GWSDAT user interface.
We acknowledge and thank the R project for Statistical Computing and all its contributors without which this project would not have been possible.
A big thank you to Shell's worldwide environmental consultants for assistance in evaluating and testing the earlier versions of GWSDAT. Thanks also to the Shell Year in Industry students who spent a great deal of time testing GWSDAT and making suggestions for improvements.
We thank both current and former colleagues including Matthew Lahvis, Jonathan Smith, George Devaull, Dan Walsh, Curtis Stanley, Marco Giannitrapani and Philip Jonathan for their support, vision and advocacy of GWSDAT.
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- Evers, L., Molinari, D. A., Bowman, A. W., Jones, W. R., Spence, M. J., (in Press). Efficient and automatic methods for flexible regression on spatiotemporal data, with applications to groundwater monitoring, Environmetrics.
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- R Development Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2008. ISBN 3-900051-07-0, http://www.r-project.org
- W. R. Jones, M. J. Spence, Matthijs Bonte. Analyzing Groundwater Quality Data and Contamination Plumes with GWSDAT. Groundwater. doi:10.1111/gwat.12340.
- Ricker, J.A. 2008. A Practical Method to Evaluate Ground Water Contaminant Plume Stability. Ground Water Monitoring & Remediation 28, no. 4: 85–94.
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