John Quilty is an Assistant Professor in the Department of Civil and Environmental Engineering at the University of Waterloo.
His research seeks to address the issues of nonlinearity, multiscale change, and uncertainty in hydrological and water resources forecasting. In this regard, Prof. Quilty has introduced new and computationally efficient information-theoretic methods for nonlinear input variable selection considering uncertainty, developed a set of best practices for wavelet-based forecasting of multiscale processes, and proposed a multiscale stochastic data-driven forecasting framework that accounts for uncertainty in each stage of the forecast design, a particularly useful method for creating ensemble forecast systems that can simultaneously perform ensemble member selection and weighting.
He is currently pursuing three research areas that explore: i) new AI, machine learning, and deep learning approaches with a focus on computational efficiency; ii) emerging uncertainty estimation methods (e.g., for input data, input variable selection, parameter, model structure, model output, etc.); and iii) coupling physical, conceptual, and data-driven models in addition to auxiliary input sources such as Numerical Weather Predictions, large scale climate indicators, remote sensing, etc. to improve hydrological and water resources forecast accuracy and reliability.
An underlying goal of Prof. Quilty’s research program is to create a unified data-driven framework drawing on Big Data sourced from smart networks that can be used to optimize, plan, and manage urban water resources systems (e.g., water supply, wastewater, and stormwater systems), and that can automatically adapt to environmental and policy changes in the face of uncertainty.
Prior to joining the Department of Civil and Environmental Engineering, Prof. Quilty worked as a Research Associate at McGill University where he focused on new AI-based methods for hydrological forecasting. At the same time, he worked as a Water Meter Operations Engineer at the City of Ottawa where he specialized in: project/program management, developing water consumption forecasting models for the purpose of establishing water, wastewater, and storm water rates, analysis of water consumption profiles for design, operational, and billing concerns, water meter right sizing, and the development of specifications and standards for water supply system components.
- Hydrological and water resources modelling, forecasting, and simulation
- Data-driven modelling (artificial intelligence, machine learning, deep learning, etc.)
- Data mining
- Time series analysis
- Big Data and Internet-of-Things applications in hydrology and water resources
- Uncertainty quantification and risk assessment
- Ensemble modelling/forecasting
- Data assimilation
- Water supply systems
- Smart water networks
- 2018, Doctorate, Bioresource Engineering, McGill University
- 2012, Bachelor's, Civil Engineering with Concentration in Management, Carleton University
- Boucher, M.-A., Quilty J., Adamowski J., 2020. Data assimilation for streamflow forecasting using extreme learning machines. Water Resources Research, 56, e2019WR026226. (Submitted in 2019)
- Quilty, J., Adamowski, J., Boucher, M.-A., 2019. A stochastic data-driven ensemble forecasting framework for water resources: A case study using ensemble members derived from a database of deterministic wavelet-based models. Water Resources Research, 55, pp. 175-202. (Submitted in 2019)
- Quilty J., Adamowski J., 2020. A stochastic wavelet-based data-driven framework for forecasting uncertain multiscale hydrological and water resources processes. Environmental Modelling and Software, 130, 104718. (Submitted in 2019)
- Mouatadid, S., Adamowski, J.F., Tiwari, M.K., Quilty J.M., 2019. Coupling the maximum overlap discrete wavelet transform and long short-term memory networks for irrigation flow forecasting. Agricultural Water Management, 219, pp. 72-85. (Submitted in 2019)
- Quilty J.,Adamowski J., 2018. Addressing the incorrect usage of wavelet-based hydrological and water resources forecasting models for real-world applications with best practices and a new forecasting framework. Journal of Hydrology, 563, pp. 336-353. (Submitted in 2018)
- Quilty J., Adamowski J., Khalil B., Rathinasamy, M., 2016. Bootstrap rank-ordered conditional mutual information (broCMI): A nonlinear input variable selection method for water resources modeling. Water Resources Research, 52, pp. 2299-2326. (Submitted in 2016)