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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