Dr. Zoe Li

B.Eng. (China)

M.A.Sc., Ph.D. (Regina)

M. De Coste*, Z. Li, and R. Khedri (2024). A hybrid ontology-based semantic and machine learning model for the prediction of spring breakup. Computer-Aided Civil and Infrastructure Engineering (Wiley), 39(2): 264-280. https://doi.org/10.1111/mice.13074

A. Elsayed, M. Ghaith*, A., Z. Li, and W. El-Dakhakhni (2024). Genetic programming expressions for effluent quality prediction: Towards AI-driven monitoring and management of wastewater treatment plants. Journal of Environmental Management (Elsevier), 356: 120510. https://doi.org/10.1016/j.jenvman.2024.120510

A. Badr*, Z. Li, and W. El-Dakhakhni (2023). Dam system and reservoir operational safety: A meta-research. Water (MDPI), 15(19): 3427. https://doi.org/10.3390/w15193427

A. Badr*, Z. Li and W. El-Dakhakhni (2023). Dynamic resilience quantification of hydropower infrastructure in a multi-hazard environment. Journal of Infrastructure Systems (American Society of Civil Engineers), 29(2): 04023012. https://doi.org/10.1061/JITSE4.ISENG-2188

Z. Chen, Y. Duan, Z. Li, Y. Zhang, L. Lin, T. Cao, K. Yao (2023). MoS2 grown on hollow carbon nanospheres as photoanode for improved photoelectrocatalytic degradation of Bisphenol A. Journal of Cleaner Production (Elsevier), 404: 136929. https://doi.org/10.1016/j.jclepro.2023.136929

M. De Coste*, Z. Li, and R. Khedri (2023). The prediction of mid-winter and spring breakups of ice cover on Canadian rivers using a hybrid ontology-based and machine learning model. Environmental Modelling and Software (Elsevier), 160: 105577. https://doi.org/10.1016/j.envsoft.2022.105577

X. Li* and Z. Li (2023). Evaluation of bias correction techniques for generating high-resolution daily temperature projections from CMIP6 models. Climate Dynamics (Springer), 61: 3893-3910. https://doi.org/10.1007/s00382-023-06778-8

F. Rao* and Z. Li (2023). A power storage system planning model for the Wolfe Island wind farm. Transactions of the Canadian Society for Mechanical Engineering (Canadian Society for Mechanical Engineering), 47(4): 551-561. Https://doi.org/10.1139/tcsme-2023-0016

E. Rowe*, Y. Guo and Z. Li (2023). A closer look at Toronto’s water quality control design criteria for bioretention cells. Canadian Journal of Civil Engineering (Canadian Society for Civil Engineering), 51(1): 1-10. https://doi.org/10.1139/cjce-2023-0148 “Editor’s Choice” paper

P. Zhou* and Z. Li (2023) Arbitrary polynomial chaos expansion for uncertainty analysis of the one-dimensional hindered-compression continuous settling model. Journal of Water Process Engineering (Elsevier), 52: 103489. https://doi.org/10.1016/j.jwpe.2023.103489

P. Zhou*, Z. Li, S. Snowling and J. Barclay (2023). Unraveling the impact of COVID-19 lockdowns on Canadian municipal sewage. Environmental Science: Water Research & Technology (Royal Society of Chemistry), 9: 2213-2218. https://doi.org/10.1039/D3EW00126A

P. Zhou*, Z. Li, Y. Zhang, S. Snowling and J. Barclay (2023). Online machine learning for stream wastewater influent flow rate prediction under unprecedented emergencies. Frontiers of Environmental Science & Engineering (Springer), 17, 152. https://doi.org/10.1007/s11783-023-1752-7 Front Cover Paper for Issue 12, 2023

P. Zhou*, Z. Li and S. A. Smyth (2022). Prediction of Bisphenol A contamination in Canadian municipal wastewater. Journal of Water Process Engineering (Elsevier), 50: 103304. https://doi.org/10.1016/j.jwpe.2022.103304

P. Zhang, Y. Cai, Y. He, Y. Xie, X. Zhang and Z. Li (2022). Changes of vegetational cover and the induced impacts on hydrological processes under climate change for a high-diversity watershed of south China. Journal of Environmental Management (Elsevier), 322: 115963. https://doi.org/10.1016/j.jenvman.2022.115963

M. De Coste*, Z. Li and Y. Dibike (2022). Assessing and predicting the severity of mid-winter breakups based on Canada-wide river ice data. Journal of Hydrology (Elsevier), 127550. https://doi.org/10.1016/j.jhydrol.2022.127550

M. De Coste*, Z. Li and Y. Dibike (2022). Machine-learning approach for predicting the occurrence and timing of mid-winter ice breakups on Canadian rivers. Environmental Modelling & Software (Elsevier), 152: 105402. https://doi.org/10.1016/j.envsoft.2022.105402.

D. Kovacs, Z. Li, B. Baetz, Y. Hong, S. Donnaz, X. Zhao, P. Zhou*, H. Ding, and Q. Dong (2022). Membrane fouling prediction and uncertainty analysis using machine learning: A wastewater treatment plant case study. Journal of Membrane Science (Elsevier), 660: 120817. https://doi.org/10.1016/j.memsci.2022.120817

X. Li* and Z. Li (2022). Global water availability and its distribution under the Coupled Model Intercomparison Project Phase Six scenarios. International Journal of Climatology (Royal Meteorological Society), 42(11): 5748-5767. https://doi.org/10.1002/joc.7559

P. Zhang, Y. Cai, Y. Xie, Y. Yi, W. Yang and Z. Li (2022). Effects of a cascade reservoir system on runoff and sediment yields in a River Basin of southwestern China. Ecological Engineering (Elsevier), 179: 106616. https://doi.org/10.1016/j.ecoleng.2022.106616

P. Zhou, C. Li, Z. Li and Y. Cai (2022). Assessing uncertainty propagation in hybrid models for daily streamflow simulation based on arbitrary polynomial chaos expansion. Advances in Water Resources (Elsevier), 160: 104110. https://doi.org/10.1016/j.advwatres.2021.104110

P. Zhou, Z. Li, S. Snowling, R. Goel and Q. Zhang (2022). Multi-step ahead prediction of hourly influent characteristics for wastewater treatment plants: a case study from North America. Environmental Monitoring and Assessment (Springer), 194: 389. https://doi.org/10.1007/s10661-022-09957-y

C. Li, Y. Cai, Z. Li, Q. Zhang, L. Sun, X. Li* and P. Zhou* (2021). Hydrological response to climate and land use changes in the dry–warm valley of the upper Yangtze River. Engineering (Elsevier). https://doi.org/10.1016/j.eng.2021.04.029

Q. Zhang*, Z. Li, L. Zhu, F. Zhang, E. Sekerinski, J. Han and Y. Zhou (2021). Real-time prediction of river chloride concentration using ensemble learning. Environmental Pollution (Elsevier). https://doi.org/10.1016/j.envpol.2021.118116

M. Ghaith*, Z. Li and B. Baetz (2021). Uncertainty analysis for hydrological models with interdependent parameters: An improved polynomial chaos expansion approach. Water Resources Research (Wiley), 57(8): e2020WR029149. https://doi.org/10.1029/2020WR029149

Q. Zhang* and Z. Li (2021). Data-driven interval credibility constrained quadratic programming model for water quality management under uncertainty. Journal of Environmental Management (Elsevier), 293(1): 112791. https://doi.org/10.1016/j.jenvman.2021.112791

M. De Coste*, Z. Li, D. Pupek and W. Sun (2021). A hybrid ensemble modelling framework for the prediction of breakup ice jams on northern rivers. Cold Regions Science and Technology (Elsevier), 189: 103302. https://doi.org/10.1016/j.coldregions.2021.103302

Z. Yan*, B. Baetz and Z. Li (2021). A cloud-based dual-objective nonlinear programming model for irrigation water allocation in Northwest China. Journal of Cleaner Production (Elsevier), 308: 127330. https://doi.org/10.1016/j.jclepro.2021.127330

W. Huang* and Z. Li (2021). Inexact credibility-constrained programming approach for electricity planning in Ontario, Canada. Journal of Energy Engineering (American Society of Civil Engineers), 147(4): 04021015. https://doi.org/10.1061/(ASCE)EY.1943-7897.0000755

E. Rowe*, Y. Guo and Z. Li (2021). Seeking more cost-efficient design criteria for infiltration trenches. Journal of Sustainable Water in the Built Environment (American Society of Civil Engineers), 7(3): 04021009. https://doi.org/10.1061/JSWBAY.0000951

Y. Wang, Z. Li, L. Liu and P. Guo (2021). A fuzzy dependent-chance interval multi-objective stochastic expected value programming approach for irrigation water resources management under uncertainty. Desalination and Water Treatment (Desalination Publications), 212: 17-30. https://doi.org/10.5004/dwt.2021.26521

Q. Zhang, Z. Li and W. Huang (2021). Simulation-based interval chance-constrained quadratic programming model for water quality management: A case study of the central Grand River in Ontario, Canada. Environmental Research (Elsevier), 192: 110206. https://doi.org/10.1016/j.envres.2020.110206

M. Ghaith* and Z. Li (2020). Propagation of parameter uncertainty in SWAT: A probabilistic forecasting method based on polynomial chaos expansion and machine learning. Journal of Hydrology (Elsevier), 586: 124854. https://doi.org/10.1016/j.jhydrol.2020.124854

M. Ghaith, A. Siam, Z. Li and W. El-Dakhakhni (2020). Hybrid hydrological data-driven approach for daily streamflow forecasting. Journal of Hydrologic Engineering (American Society of Civil Engineers), 25(2): 04019063. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001866

X. Li, Z. Li, W. Huang and P. Zhou* (2020). Performance of statistical and machine learning ensembles for daily temperature downscaling. Theoretical and Applied Climatology (Springer), 140: 571–588. https://doi.org/10.1007/s00704-020-03098-3

Y. Li, Y. Cai, Z. Li, X. Wang, Q. Fu, D. Liu, L. Sun and R. Xu (2020). An approach for runoff and sediment nexus analysis under multi-flow conditions in a hyper-concentrated sediment river, Southwest China. Journal of Contaminant Hydrology (Elsevier). https://doi.org/10.1016/j.jconhyd.2020.103702

Y. Wang, Z. Li, S. Guo, F. Zhang and P. Guo (2020). A risk-based fuzzy boundary interval two-stage stochastic water resources management programming approach under uncertainty. Journal of Hydrology (Elsevier), 582: 124553. https://doi.org/10.1016/j.jhydrol.2020.124553

Z. Yan*, M. Li and Z. Li (2020). Efficient and economical allocation of irrigation water under a changing environment: A stochastic multi-objective nonlinear programming model. Irrigation and Drainage (Wiley). https://doi.org/10.1002/ird.2523 Wiley Top Cited Article 2020-2021 and Wiley Top Cited Article 2021-2022

Q. Zhang* and Z. Li (2020). Development of an interval quadratic programming water quality management model and its solution algorithms. Journal of Cleaner Production (Elsevier), 249: 119319. https://doi.org/10.1016/j.jclepro.2019.119319

G. Boyd, D. Na, Z. Li, S. Snowling, Q. Zhang* and P. Zhou* (2019). Influent forecasting for wastewater treatment plants in North America. Sustainability (MDPI), 11(6): 1764. https://doi.org/10.3390/su11061764

X. Li, Z. Li, Q. Zhang, P. Zhou* and W. Huang* (2019). Prediction of long-term near-surface temperature based on NA-CORDEX output. Journal of Environmental Informatics Letters (International Society of Environmental Information Sciences), 2(1): 10-18. https://doi.org/10.3808/jeil.201900012

Z. Li, G. Huang, L. Guo, Y. Fan and J. Chen (2019). A fuzzy gradient chance-constrained evacuation model for managing risks of nuclear power plants under multiple uncertainties. Journal of Environmental Informatics (International Society of Environmental Information Sciences), 33(2): 129-138. https://doi.org/10.3808/jei.201500315

B. Shan, P. Guo, S. Guo and Z. Li (2019). A price-forecast-based irrigation scheduling optimization model under the response of fruit quality and price to water. Sustainability (MDPI), 11(7): 2124. https://doi.org/10.3390/su11072124

X. Zeng, J. Zhang, L. Yu, J. Zhu, Z. Li and L. Tang (2019). A sustainable water-food-energy plan to confront climatic and socioeconomic changes using simulation-optimization approach. Applied Energy (Elsevier), 236: 743-759. https://doi.org/10.1016/j.apenergy.2018.11.086

Y. Zhai, G. Huang, X. Wang, X. Zhou, C. Lu and Z. Li (2019). Future projections of temperature changes in Ottawa, Canada through stepwise clustered downscaling of multiple GCMs under RCPs. Climate Dynamics (Springer), 52(5-6): 3455-3470. https://doi.org/10.1007/s00382-018-4340-y

Q. Zhang, Z. Li, S. Snowling, A. Siam and W. El-Dakhakhni (2019). Predictive models for wastewater flow forecasting based on time series analysis and artificial neural network. Water Science and Technology (International Water Association), 80(2): 243-253. https://doi.org/10.2166/wst.2019.263

P. Zhou, Z. Li, S. Snowling, B. W. Baetz, D. Na and G. Boyd* (2019). A random forest model for inflow prediction at wastewater treatment plants. Stochastic Environmental Research and Risk Assessment (Springer), 33(10): 1781-1792. https://doi.org/10.1007/s00477-019-01732-9

P. Zhou, Z. Li, S. Snowling, R. Goel and Q. Zhang (2019). Short-term wastewater influent prediction based on random forests and multi-layer perceptron. Journal of Environmental Informatics Letters (International Society of Environmental Information Sciences), 1(2): 87-93. https://doi.org/10.3808/jeil.201900010

View more publications.