Publications

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Author Title Type [ Year(Asc)]
2024
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
2023
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
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
2022
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
Jahangir, M. Sina, Biazar, S. Mostafa, Hah, D. , Quilty, J. , & Isazadeh, M. . (2022). Investigating the impact of input variable selection on daily solar radiation prediction accuracy using data-driven models: a case study in northern Iran. Stochastic Environmental Research and Risk Assessment, 36(1), 225-249. Retrieved from https://link.springer.com/article/10.1007/s00477-021-02070-5
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
2021
Jahangir, M. Sina, & Quilty, J. . (2021). Improving hydrological forecasts through temporal hierarchal reconciliation. EGU General Assembly 2021. Retrieved from https://meetingorganizer.copernicus.org/EGU21/EGU21-13303.html
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
Barzegar, R. , Adamowski, J. , & Quilty, J. . (2021). Improving Deep Learning hydrological time series modeling using Gaussian Filter preprocessing. EGU General Assembly 2021. Retrieved from https://meetingorganizer.copernicus.org/EGU21/EGU21-1644.html
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
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
Barzegar, R. , Razzagh, S. , Quilty, J. , Adamowski, J. , Pour, H. K. , & Booij, M. J. . (2021). Improving GALDIT-based groundwater vulnerability predictive mapping using coupled resampling algorithms and machine learning models. Journal of Hydrology, 598, 126370. Retrieved from https://www.sciencedirect.com/science/article/abs/pii/S0022169421004170
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
2020
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.
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.
Boucher, M. - A. , Quilty, J. , & Adamowski, J. . (2020). Data assimilation for streamflow forecasting using extreme learning machines. Water Resources Research, 56, e2019WR026226.
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
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
2019
Ghaemi, A. , Rezaie-Balf, M. , Adamowski, J. , Kisi, O. , & Quilty, J. . (2019). On the applicability of maximum overlap discrete wavelet transform integrated with MARS and M5 model tree for monthly pan evaporation prediction. Agricultural and Forest Meteorology, 278, 107647. Elsevier.
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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