Publications

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Author [ Title(Desc)] Type Year
A
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.
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.
Ciupak, M. , Ozga-Zielinski, B. , Adamowski, J. , Quilty, J. , & Khalil, B. . (2015). The application of dynamic linear bayesian models in hydrological forecasting: varying coefficient regression and discount weighted regression. Journal of Hydrology, 530, 762–784. Elsevier.
B
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. , 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.
C
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
Belayneh, A. , Adamowski, J. , Khalil, B. , & Quilty, J. . (2016). Coupling machine learning methods with wavelet transforms and the bootstrap and boosting ensemble approaches for drought prediction. Atmospheric research, 172, 37–47. 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.
D
Boucher, M. - A. , Quilty, J. , & Adamowski, J. . (2020). Data assimilation for streamflow forecasting using extreme learning machines. Water Resources Research, 56, e2019WR026226.
E
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.
F
Belayneh, A. , Adamowski, J. , & Khalil, B. . (2013). Forecasting drought via bootstrap and machine learning methods. In CSCE 3rd Specialty Conference on Disaster Prevention and Mitigation.
Deo, R. C. , Tiwari, M. K. , Adamowski, J. F. , & Quilty, J. M. . (2017). Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model. Stochastic environmental research and risk assessment, 31, 1211–1240. Springer Berlin Heidelberg.
Adamowski, J. F. , Quilty, J. , Khalil, B. , & Rathinasamy, M. . (2014). Forecasting Urban Water Demand via Machine Learning Methods Coupled with a Bootstrap Rank-Ordered Conditional Mutual Information Input Variable Selection Method. In AGU Fall Meeting Abstracts.
G
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
H
Boucher, M. - A. , Quilty, J. , & Adamowski, J. . (2017). Hydrological data assimilation using Extreme Learning Machines. In EGU General Assembly Conference Abstracts (Vol. 19, p. 5722).
I
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
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
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. , 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).
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

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