Publications

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Author [ Title(Desc)] Type Year
L
Quilty, J. M. , & Sikorska-Senoner, A. E. . (2021). Learning from one’s errors: A data-driven approach for mimicking an ensemble of hydrological model residuals. EGU General Assembly 2021. Retrieved from https://meetingorganizer.copernicus.org/EGU21/EGU21-13244.html
M
Quilty, J. , & Adamowski, J. . (2021). A maximal overlap discrete wavelet packet transform integrated approach for rainfall forecasting–A case study in the Awash River Basin. Environmental Modelling & Software, 105119. Retrieved from https://www.sciencedirect.com/science/article/abs/pii/S1364815221001626
Goyal, M. Kumar, Bharti, B. , Quilty, J. , Adamowski, J. , & Pandey, A. . (2014). Modeling of daily pan evaporation in sub tropical climates using ANN, LS-SVR, Fuzzy Logic, and ANFIS. Expert systems with applications, 41, 5267–5276. Elsevier.
Rahman, A. T. M. S. , Hosono, T. , Quilty, J. M. , Das, J. , & Basak, A. . (2020). Multiscale groundwater level forecasting: Coupling new machine learning approaches with wavelet transforms. Advances in Water Resources, 141, 103595.
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Sikorska-Senoner, A. E. , & Quilty, J. M. . (2021). A novel ensemble-based conceptual-data-driven approach for improved streamflow simulations. Environmental Modelling & Software, 105094. Retrieved from https://www.sciencedirect.com/science/article/pii/S1364815221001377
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Rezaali, M. , Quilty, J. , & Karimi, A. . (2021). Probabilistic urban water demand forecasting using wavelet-based machine learning models. Journal of Hydrology, 126358. Retrieved from https://www.sciencedirect.com/science/article/abs/pii/S0022169421004054
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Jahangir, M. S. , You, J. , & Quilty, J. . (2023). A quantile-based encoder-decoder framework for multi-step ahead runoff forecasting. Journal of Hydrology, 619, 129269. Retrieved from https://www.sciencedirect.com/science/article/pii/S0022169423002111
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Quilty, J. M. , Sikorska-Senoner, A. E. , & Hah, D. . (2022). A stochastic conceptual-data-driven approach for improved hydrological simulations. Environmental Modelling & Software, 149, 105326. Retrieved from https://www.sciencedirect.com/science/article/pii/S1364815222000329
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, 175–202.
Quilty, J. , & Adamowski, J. . (2018). A stochastic data-driven forecasting framework using wavelets for forecasting uncertain hydrological and water resources processes. In EGU General Assembly Conference Abstracts (Vol. 20, p. 2380).
Quilty, J. , & Adamowski, J. . (2020). A stochastic wavelet-based data-driven framework for forecasting uncertain multiscale hydrological and water resources processes. Environmental Modelling & Software, 130, 104718.
Quilty, J. , & Adamowski, J. F. . (2015). Stochastic weather inputs for improved urban water demand forecasting: application of nonlinear input variable selection and machine learning methods. In AGU Fall Meeting Abstracts.
Yaseen, Z. Mundher, Jaafar, O. , Deo, R. C. , Kisi, O. , Adamowski, J. , Quilty, J. , & El-Shafie, A. . (2016). Stream-flow forecasting using extreme learning machines: A case study in a semi-arid region in Iraq. Journal of Hydrology, 542, 603–614. Elsevier.
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Barzegar, R. , Adamowski, J. , Quilty, J. , & Aalami, M. Taghi. (2020). Using a boundary-corrected wavelet transform coupled with machine learning and hybrid deep learning approaches for multi-step water level forecasting in Lakes Michigan and Ontario. EGU General Assembly Conference Abstracts, 4233. Retrieved from https://scholar.google.ca/scholar?oi=bibs&cluster=8698411343122623888&btnI=1&hl=en
Barzegar, R. , Ghasri, M. , Qi, Z. , Quilty, J. , & Adamowski, J. . (2019). Using Bootstrap ELM and LSSVM Models to Estimate River Ice Thickness in the Mackenzie River Basin in the Northwest Territories, Canada. Journal of Hydrology. Elsevier.
Roy, D. , Barzegar, R. , Quilty, J. , & Adamowski, J. . (2020). Using ensembles of adaptive neuro-fuzzy inference system and optimization algorithms to predict reference evapotranspiration in subtropical climatic zones. Journal of Hydrology, 591, 125509. Retrieved from https://scholar.google.ca/scholar?oi=bibs&cluster=15103368238207897622&btnI=1&hl=en
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Deo, R. C. , Downs, N. , Parisi, A. V. , Adamowski, J. F. , & Quilty, J. M. . (2017). Very short-term reactive forecasting of the solar ultraviolet index using an extreme learning machine integrated with the solar zenith angle. Environmental research, 155, 141–166. Academic Press.

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