Publications

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[ Author(Asc)] Title Type Year
Y
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.
S
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
R
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
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
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.
Q
Quilty, J. , Jahangir, M. S. , You, J. , Hughes, H. , Hah, D. , & Tzoganakis, I. . (2023). Bayesian extreme learning machines for hydrological prediction uncertainty. Journal of Hydrology, 626, 130138. Retrieved from https://www.sciencedirect.com/science/article/pii/S0022169423010806
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. 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
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
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. , & 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. , Khalil, B. , & Rathinasamy, M. . (2018). Information-theoretic-based input variable selection for hydrology and water resources. In EGU General Assembly Conference Abstracts (Vol. 20, p. 2379).
Quilty, J. . (2018). An Ensemble Wavelet-based Stochastic Data-driven Framework for Addressing Nonlinearity, Multiscale Change, and Uncertainty in Water Resources Forecasting. McGill University Libraries.
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, 336–353. Elsevier.
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, 2299–2326.
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.
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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, 72–85. Elsevier.
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Khosravi, K. , Phuong, T. T. , Barzegar, R. , Quilty, J. , & Alami, M. T. . (2022). Comparing the Soil Conservation Service model with new machine learning algorithms for predicting cumulative infiltration in semi-arid regions. Pedosphere. Retrieved from https://www.sciencedirect.com/science/article/pii/S1002016022000157
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Jahangir, M. S. , & Quilty, J. . (2024). Generative deep learning for probabilistic streamflow forecasting: conditional variational auto-encoder. Journal of Hydrology, 629, 130498. Retrieved from https://www.sciencedirect.com/science/article/pii/S0022169423014403

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