Publications

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[ Author(Desc)] Title Type Year
A
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.
B
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
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.
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.
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.
Boucher, M. - A. , Quilty, J. , & Adamowski, J. . (2020). Data assimilation for streamflow forecasting using extreme learning machines. Water Resources Research, 56, e2019WR026226.
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).
C
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.
D
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.
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.
G
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.
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.
J
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
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
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
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
K
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
M
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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