A Deep Surrogate Model for Water Quality Measurement

Dr Yifan Zhang1, Dr Peter Thorburn1

1Csiro, brisbane, Australia


For large-scale automated, water quality monitoring, some physical or chemical parameters are unable to be measured directly due to financial or environmental limitations. As an example, the cost of high accuracy measurement of nitrogen is prohibitive, and one can only measure nitrogen in creeks and rivers at selected locations. If nitrate concentrations are related to some other, more readily measured water parameters, it may be possible to use these parameters (“surrogates”) to estimate nitrogen concentrations.

We propose a deep surrogate model (DSM) for indirect nitrogen measurement in large-scale water quality monitoring networks. The DSM applies a stacked denoising autoencoder to extract the features of the water quality surrogates. This strategy allows one to utilize all the sensory data across the monitoring network, which can significantly extend the size of training data.

Furthermore, instead of only learning the regression relationship between water quality surrogates and the nitrogen concentration in the source stations, the DSM is designed to gain the sensor data distribution differences between the source and target stations. In this approach, the training of DSM can be guided by acknowledging the information from the target station.

We evaluate the DSM by using real-world time series data from a wireless water quality monitoring network in Australia. Compared to models based on Support Vector Machine and Artificial Neural Network, the DSM achieves up to 49.0% and 42.4% improvements regarding the RMSE and MAE respectively. Hence, the DSM is an attractive strategy for generating the estimated nitrogen concentration for large-scale environmental monitoring projects.


Yi-Fan Zhang is a Postdoctoral Fellow in CSIRO, Digiscape Future Science Platform. As a machine learning researcher, he has participated in several CSIRO projects in areas related to water quality forecasting, anomaly detection, sensor data imputation and crop yield forecasting. He received his Bachelor of System Engineering and Master of Software Engineering from Beijing University of Aeronautics and Astronautics, Beijing, China in 2008 and 2011, respectively, and the Ph.D degree in data science from Queensland University of Technology, Brisbane, Australia, in 2016. His research interests include artificial intelligence, time series modeling, cloud computing and the Internet of Things.


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