Publications

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Author Title [ Type(Desc)] Year
Journal Article
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
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Presentation
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
Thesis
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.

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