Publications Database
Welcome to the new Schulich Peer-Reviewed Publication Database!
The database is currently in beta-testing and will be updated with more features as time goes on. In the meantime, stakeholders are free to explore our faculty’s numerous works. The left-hand panel affords the ability to search by the following:
- Faculty Member’s Name;
- Area of Expertise;
- Whether the Publication is Open-Access (free for public download);
- Journal Name; and
- Date Range.
At present, the database covers publications from 2012 to 2020, but will extend further back in the future. In addition to listing publications, the database includes two types of impact metrics: Altmetrics and Plum. The database will be updated annually with most recent publications from our faculty.
If you have any questions or input, please don’t hesitate to get in touch.
Search Results
M. Kozlova, A. Ahola, P. Roy, J.S. Yeomans (2025). "Simple Binning Algorithm and SimDec Visualization for Comprehensive Sensitivity Analysis of Complex Computational Models", Journal of Environmental Informatics Letters, 13, 1, 38-56.
Abstract
Models of complex environmental systems inherently contain interactions and dependencies among their input variables that affect their joint influence on the output. Such models are often computationally expensive and few sensitivity analysis methods can effectively process such complexities. Moreover, the sensitivity analysis field as a whole pays limited attention to the nature of interaction effects, whose understanding can prove to be critical for the design of safe and reliable systems. In this paper, we introduce and extensively test a simple binning approach for computing sensitivity indices and demonstrate how complementing it with the smart visualization method, simulation decomposition (SimDec), can permit important insights into the behaviour of complex models. The straightforward binning computation generates first-, second-order effects, and a combined sensitivity index, and is considerably more computationally efficient than the “industry standard” measure for Sobol’ indices introduced by Saltelli et al. The cases vary from business and engineering to environmental applications. The totality of the sensitivity analysis framework provides an efficient and intuitive way to analyze the behaviour of complex environmental systems containing interactions and dependencies.Kozlova, M. and Yeomans, J.S. (2022). "Sustainability Analysis and Environmental Decision-Making Using Simulation, Optimization, and Computational Analytics", Sustainability, 14(3), 1655.
Abstract
This issue contains applied computational analytics papers that either create new methods or provide innovative applications of existing methods to assist with sustainability analysis and environmental decision-making applications. In practice, environmental analytics is an integra-tion of science, methods, and techniques that involves a combination of computers, computational intelligence, information technology, mathematical modelling, and system science to assess re-al-world, sustainability, and environmental problems. The contributions to this issue all inves-tigate novel approaches of computational analytics – modelling, computational solution proce-dures, optimization, simulation, and technologies—as applied to sustainability analysis. The papers emphasize both the practical relevance and the methodological contributions of the work to environmental decision-making. Areas of application encompass a wide spectrum of environ-mental decision-making and sustainability, from waste, water, energy, climate change, industrial ecology, resource recovery, to recycling.Gunalay, Y. and Yeomans, J.S. (2018). "A Simultaneous, Simulation-Optimization Modelling-to-Generate-Alternatives Approach for Stochastic Water Resources Management Decision-Making", International Journal of Advancement in Engineering Technology, Management and Applied Science, 3(1), 57-73.