Dr Stephanie Clark

I am a water engineer/researcher with a particular interest in improving the extraction and visualisation of meaningful information from water-related data. At the intersection of machine learning, statistics and environmental science, the focus of my research is on the improved abstraction of patterns and relationships in real-world environmental data with the goal of increasing our understanding of current conditions and aiding predictions of water availability in the future.

My research involves supervised and unsupervised neural networks, deep learning, time-series prediction, transfer learning, hybrid models, dimension reduction, clustering, data visualisation and visual analytics for addressing issues related to water resources. Of particular interest are patterns that cross interdisciplinary boundaries, such as anthropogenic influences on water resource systems or environmental impacts on health.

Selected publications:

Clark, S. R., Lerat, J., Perraud, J.-M., and Fitch, P. (2023). Deep learning for monthly rainfall-runoff modelling: a comparison with conceptual models across Australia. Hydrology & Earth System Sciences Discussions [in press]

Clark, S. R., Pagendam, D., & Ryan, L. (2022). Forecasting multiple groundwater time series with local and global deep learning networks. International Journal of Environmental Research and Public Health, 19(9), 5091. Part of special issue: Statistical Advances in Environmental Sciences

Clark, S. R. (2022). Unravelling groundwater time series patterns: Visual analytics-aided deep learning in the Namoi region of Australia. Environmental Modelling & Software, 105295.

Clark, S., Hyndman, R. J., Pagendam, D., & Ryan, L. (2020). Modern strategies for time series regression. International Statistical Review, 88, S179-S204. Part of special issue: Data Science vs. Classical Inference

Clark, S., Sisson, S.A. and Sharma, A., (2020). Tools for enhancing the application of self-organizing maps in water resources research and engineering. Advances in Water Resources, 143, p.103676. Part of special issue: Machine Learning for Water Resources and Subsurface Systems

Clark, S., Sisson, S. A., & Sharma, A. (2017). Nonlinear manifold representation in natural systems. Environmental Modelling & Software, 89, 61-76.

Clark, S., Sarlin, P., Sharma, A. and Sisson, S.A., (2015). Increasing dependence on foreign water resources? An assessment of trends in global virtual water flows using a self-organizing time map. Ecological Informatics, 26, pp.192-202. Part of special issue: Information and Decision Support Systems for Agriculture and Environment