Revolutionising water quality monitoring in the information age

This is an Australian Research Council (ARC) Linkage Project

Project Overview & Aims

In today’s information age, automated, low-cost, in-situ sensors are revolutionising the way we monitor and manage the environment.

In-situ sensors produce high-volume, high-velocity water quality data describing fine-scale patterns, trends and extremes throughout river networks. There is great potential for data from in-situ sensors to improve our understanding and management of water resources, biodiversity, agriculture and industry, but numerous challenges must be addressed before these benefits can be achieved. For example, the

  • data are often prone to errors caused by miscalibration, biofouling, battery and technical failures;
  • technical anomalies and the ability to detect them can differ according to the geographic characteristics of the environmental system and spatial placement of the sensors;
  • sheer volume and velocity of data mean we can no longer continue the current practice of manual anomaly identification; and
  • locations within river networks have unique spatial relationships (e.g. branching network structure, within-network connectivity, flow direction, and flow volume) that must be accounted for in statistical models.

Our aim is to develop novel statistical methods to detect technical anomalies in high-frequency in-situ sensor data collected on branching river networks using computationally efficient spatio-temporal models; with the applied goals of automating anomaly detection in water-quality data and generating predictions of sediment and nutrient concentrations throughout river networks in near-real time.

Our research is broken into five activity areas:

  • Activity 1: Investigate relationships between water-quality parameters collected using in-situ sensor data from different river networks, and use those relationships to distinguish between technical anomalies and real water quality events;
  • Activity 2: Develop new methods for detecting technical anomalies (e.g. miscalibration, biofouling, battery and technical failures) in near real-time in-situ sensor data;
  • Activity 3: Develop space-time models based on in-situ sensor data that can be used to predict at unsampled locations and/or times;
  • Activity 4: Develop adaptive sampling designs for river networks to optimise the deployment of in-situ sensors; and
  • Activity 5: Build skills and capacity in partner organisations through real-time monitoring workflows and open source tools, ensuring that new methods lead to useful industry outcomes.

This interdisciplinary research addresses a significant research gap relevant to our partner organisations, and any organisation transitioning to environmental monitoring using spatially distributed, low-cost in-situ sensors.

  • Better adaptive management of natural resources
  • Clean trustworthy and comprehensive data from in-situ sensors in near real time
  • Improved scientific understanding of assisted pollutant source identification, quantitative real-time feedback for landholders
  • Optimal placement of low-and high-cost sensors that trade off costs and information gained
  • New research that enhances national and international water resource management

Project Team

Nov. 2021 Project Workshop at QUT & Online
  • Professor Kerrie Mengersen (Project Leader) Queensland University of Technology
  • Katie Buchhorn, Queensland University of Technology
  • Dr Puwasala Gamakumara, Monash University
  • Professor Rob Hyndman, Monash University
  • Professor Jay Jones, University of Alaska
  • Dr Claire Kermorvant, University of Pau
  • Dr Catherine Leigh, RMIT University
  • Professor Benoit Liquet-Weiland, Macquarie University
  • Professor James McGree, Queensland University of Technology
  • Dr Catherine Neelamraju, Queensland Department of Environment and Science
  • Dr Erin Peterson, EP Consulting and Queensland University of Technology
  • Dr Emily Saeck, Healthy Land and Water
  • Dr Edgar Santos-Fernandez, Queensland University of Technology
  • Dr Priyanga Dilini Talagala, University of Moratuwa
  • Dr Ryan Turner, University of Queensland
  • Valentina di Marco, Monash University
  • Rachel White, RMIT University

Other Collaborators

  • Dr Dan Isaak, Rocky Mountain Research Station, US Forest Service
  • Dr Guy Litt, US National Ecological Observatory Network (NEON)
  • Dr Jay Ver Hoef, Marine Mammal Laboratory, US NOAA-NMFS Alaska Fisheries Science Center



Kermorvant, C., Liquet, B., Litt, G., Jones, J.B., Mengersen, K., Peterson, E.E., Hyndman, R.J. and Leigh, C., 2021. Reconstructing missing and anomalous data collected from high-frequency in-situ sensors in fresh waters. International Journal of Environmental Research and Public Health, 18(23), p.12803.

Kermorvant C., Liquet B., Litt G., Mengersen K., Peterson E.E., Hyndman R., Jones Jr. J.B., and Leigh C. (In Review) Understanding links between water-quality variables and nitrate concentration in freshwater streams using high-frequency sensor data.

Leigh, C., Alsibai, O., Hyndman, R.J., Kandanaarachchi, S., King, O.C., McGree, J.M., Neelamraju, C., Strauss, J., Talagala, P.D., Turner, R.D., Mengersen, K. and Peterson, E.E., 2019. A framework for automated anomaly detection in high frequency water-quality data from in situ sensors. Science of The Total Environment, 664, pp.885-898.

Leigh, C., Kandanaarachchi, S., McGree, J.M., Hyndman, R.J., Alsibai, O., Mengersen, K. and Peterson, E.E., 2019. Predicting sediment and nutrient concentrations from high-frequency water-quality data. PloS One, 14(8), p.e0215503.

Pearse, A.R., McGree, J.M., Som, N.A., Leigh, C., Maxwell, P., Ver Hoef, J.M. and Peterson, E.E., 2020. SSNdesign—An R package for pseudo-Bayesian optimal and adaptive sampling designs on stream networks. PloS One, 15(9), p.e0238422.

Rodriguez-Perez, J., Leigh, C., Liquet, B., Kermorvant, C., Peterson, E., Sous, D. and Mengersen, K., 2020. Detecting technical anomalies in high-frequency water-quality data using artificial neural networks. Environmental Science & Technology, 54(21), pp.13719-13730.

Santos-Fernandez, E., Ver Hoef, J.M., Peterson, E.E., McGree J., Isaak, D.J. and Mengersen, K., 2022. Bayesian spatio-temporal models for stream networks, Computational Statistics & Data Analysis. Volume 170, 107446.

Santos-Fernandez, E., Ver Hoef, J.M., McGree, J.M., Isaak, D.J., Mengersen, K. and Peterson, E.E., 2022. SSNbayes: An R package for Bayesian spatio-temporal modelling on stream networks. (under review).

Talagala, P.D., Hyndman, R.J., Leigh, C., Mengersen, K. and Smith‐Miles, K., 2019. A feature‐based procedure for detecting technical outliers in water‐quality data from in situ sensors. Water Resources Research, 55(11), pp.8547-8568.

Buchhorn, K., Mengersen, K., Santos-Fernandez, E., Peterson, E.E., and McGree, J.M., In Review. Bayesian design with sampling windows for complex spatial processes.

Open Source Software, training materials and example datasets

oddwater – R package: Feature-based outlier detection in data from water-quality sensors described in Talagala et al. (2019)
conduits – R package: Conditional normalisation of time series data and graphical tools for visualisation. conduits can also be used to estimate the time delay between two sensor locations in rivers. For an overview, watch this demo of conduits and follow along with the R code.
SSNbayes – R package: Bayesian spatio-temporal models for data collected on stream networks, as described in Santos-Fernandez et al. 2022 and Santos-Fernandez et al. (In Review).

Watch this SSNbayes presentation and follow along in the tutorial, for a more personal overview of the functionality.


SSNdatasets – R package: containing datasets to recreate the examples in the SSNBayes vignette using the SSNBayes package.


Project Stakeholder Workshop – December 2021Playlist of videos from December 2021 Project Workshop