John Quilty

2.7k total citations
33 papers, 2.2k citations indexed

About

John Quilty is a scholar working on Environmental Engineering, Water Science and Technology and Global and Planetary Change. According to data from OpenAlex, John Quilty has authored 33 papers receiving a total of 2.2k indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Environmental Engineering, 18 papers in Water Science and Technology and 15 papers in Global and Planetary Change. Recurrent topics in John Quilty's work include Hydrological Forecasting Using AI (28 papers), Hydrology and Watershed Management Studies (17 papers) and Energy Load and Power Forecasting (8 papers). John Quilty is often cited by papers focused on Hydrological Forecasting Using AI (28 papers), Hydrology and Watershed Management Studies (17 papers) and Energy Load and Power Forecasting (8 papers). John Quilty collaborates with scholars based in Canada, Iran and India. John Quilty's co-authors include Jan Adamowski, Ravinesh C. Deo, Mukesh Tiwari, Bahaa Khalil, Özgür Kişi, Ashish Pandey, Birendra Bharti, Manish Kumar Goyal, Anna E. Sikorska‐Senoner and Rahim Barzegar and has published in prestigious journals such as SHILAP Revista de lepidopterología, Water Resources Research and Journal of Hydrology.

In The Last Decade

John Quilty

32 papers receiving 2.1k citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
John Quilty 1.5k 980 977 454 321 33 2.2k
Alireza Moghaddam Nia 1.4k 0.9× 1.0k 1.0× 1.1k 1.1× 250 0.6× 282 0.9× 52 2.3k
Ali Danandeh Mehr 1.8k 1.1× 1.6k 1.7× 1.4k 1.5× 416 0.9× 346 1.1× 115 3.3k
Rana Muhammad Adnan 1.6k 1.0× 1.1k 1.1× 1.3k 1.3× 491 1.1× 450 1.4× 71 2.7k
Paresh Chandra Deka 1.0k 0.7× 628 0.6× 692 0.7× 241 0.5× 242 0.8× 44 2.0k
Mohammad Ali Ghorbani 1.8k 1.2× 1.0k 1.1× 1.0k 1.0× 448 1.0× 470 1.5× 85 3.0k
Robert J. Abrahart 1.8k 1.2× 1.3k 1.3× 1.7k 1.8× 315 0.7× 224 0.7× 60 2.5k
P. C. Nayak 1.2k 0.8× 1.0k 1.0× 919 0.9× 277 0.6× 203 0.6× 22 1.8k
Babak Mohammadi 1.5k 1.0× 1.3k 1.3× 1.0k 1.1× 458 1.0× 521 1.6× 85 2.9k
Mohammad Taghi Sattari 858 0.6× 689 0.7× 650 0.7× 206 0.5× 228 0.7× 68 1.6k
Saeed Samadianfard 951 0.6× 630 0.6× 670 0.7× 230 0.5× 290 0.9× 54 1.9k

Countries citing papers authored by John Quilty

Since Specialization
Citations

This map shows the geographic impact of John Quilty's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by John Quilty with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John Quilty more than expected).

Fields of papers citing papers by John Quilty

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by John Quilty. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by John Quilty. The network helps show where John Quilty may publish in the future.

Co-authorship network of co-authors of John Quilty

This figure shows the co-authorship network connecting the top 25 collaborators of John Quilty. A scholar is included among the top collaborators of John Quilty based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with John Quilty. John Quilty is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Quilty, John, et al.. (2025). Hierarchical Deep Learning for Consistent Multi‐Timescale Hydrological Forecasting. Water Resources Research. 61(7). 1 indexed citations
2.
Quilty, John, et al.. (2024). Sequence-to-Sequence Deep Learning for Urban Water Demand Forecasting. SHILAP Revista de lepidopterología. 41–41.
3.
Quilty, John, et al.. (2023). A quantile-based encoder-decoder framework for multi-step ahead runoff forecasting. Journal of Hydrology. 619. 129269–129269. 39 indexed citations
4.
Quilty, John, et al.. (2023). Generative deep learning for probabilistic streamflow forecasting: Conditional variational auto-encoder. Journal of Hydrology. 629. 130498–130498. 14 indexed citations
5.
Quilty, John, et al.. (2023). Bayesian extreme learning machines for hydrological prediction uncertainty. Journal of Hydrology. 626. 130138–130138. 11 indexed citations
6.
7.
Sikorska‐Senoner, Anna E. & John Quilty. (2021). A novel ensemble-based conceptual-data-driven approach for improved streamflow simulations. Environmental Modelling & Software. 143. 105094–105094. 57 indexed citations
8.
Quilty, John, et al.. (2021). Probabilistic urban water demand forecasting using wavelet-based machine learning models. Journal of Hydrology. 600. 126358–126358. 38 indexed citations
9.
Rahman, A. T. M. Sakiur, Takahiro Hosono, John Quilty, Jayanta Das, & Amiya Basak. (2020). Multiscale groundwater level forecasting: Coupling new machine learning approaches with wavelet transforms. Advances in Water Resources. 141. 103595–103595. 140 indexed citations
10.
Roy, Dilip Kumar, Rahim Barzegar, John Quilty, & Jan Adamowski. (2020). Using ensembles of adaptive neuro-fuzzy inference system and optimization algorithms to predict reference evapotranspiration in subtropical climatic zones. Journal of Hydrology. 591. 125509–125509. 67 indexed citations
11.
Barzegar, Rahim, et al.. (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. 577. 123903–123903. 49 indexed citations
12.
Ghaemi, Alireza, Mohammad Rezaie-Balf, Jan Adamowski, Özgür Kişi, & John Quilty. (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. 107647–107647. 93 indexed citations
13.
Adamowski, Jan, et al.. (2019). Coupling the maximum overlap discrete wavelet transform and long short-term memory networks for irrigation flow forecasting. Agricultural Water Management. 219. 72–85. 88 indexed citations
14.
Quilty, John. (2018). An ensemble wavelet-based stochastic data-driven framework for addressing nonlinearity, multiscale change, and uncertainty in water resources forecasting. eScholarship@McGill (McGill). 1 indexed citations
15.
Deo, Ravinesh C., Nathan Downs, Alfio V. Parisi, Jan Adamowski, & John Quilty. (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. 78 indexed citations
16.
Keeney, John, et al.. (2017). Using the COMPA autonomous architecture for mobile network security. Zenodo (CERN European Organization for Nuclear Research). 747–753. 6 indexed citations
17.
Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Zaher Mundher, Othman Jaafar, Ravinesh C. Deo, et al.. (2016). Stream-flow forecasting using extreme learning machines: A case study in a semi-arid region in Iraq. Journal of Hydrology. 542. 603–614. 268 indexed citations
18.
Belayneh, Anteneh, Jan Adamowski, Bahaa Khalil, & John Quilty. (2016). Coupling machine learning methods with wavelet transforms and the bootstrap and boosting ensemble approaches for drought prediction. Atmospheric Research. 172-173. 37–47. 151 indexed citations
19.
Deo, Ravinesh C., Mukesh Tiwari, Jan Adamowski, & John Quilty. (2016). Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model. Stochastic Environmental Research and Risk Assessment. 31(5). 1211–1240. 203 indexed citations
20.
Goyal, Manish Kumar, Birendra Bharti, John Quilty, Jan Adamowski, & Ashish Pandey. (2014). Modeling of daily pan evaporation in sub tropical climates using ANN, LS-SVR, Fuzzy Logic, and ANFIS. Expert Systems with Applications. 41(11). 5267–5276. 251 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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