Mission

CANSIM is dedicated to innovation, training, development, and promotion: Innovation of new approaches, techniques, and tools for numerical prediction and simulation of natural and human-made systems; training of highly qualified researchers in water resources, structures and materials, and geotechnical engineering; and development and promotion of innovative sustainability-oriented solutions to various environmental and civil engineering problems. 

Capabilities


  • Hydrologic predictions/forecast using pattern recognition and soft-computing techniques (e.g., artificial neural networks, genetic algorithms, fuzzy theory, wavelet analysis)
  • Watershed simulation including rainfall-runoff modeling and soil moisture dynamics using system dynamics and HSPF
  • Modeling dynamic natural and man-made environmental (mainly water resources) systems using system dynamics (SD) approach
  • Watershed and surface water quality management using total maximum daily loads (TMDLs) approach
  • Decision analysis approach to address the sustainability of water resources and environmental systems
  • Addressing uncertainties and value of information and models in hydrology using Bayesian approach
  • Statistical and probabilistic analysis of water resources systems
  • Innovative techniques to improve engineering education

People

Faculty

Amin Elshorbagy [Water Resources]
Mohamed Boulfiza [Structures and Material]
Jitendrapal Sharma [Geotechnical]

Research Assistants

  • Antarpreet Jutla - Water resources (M.Sc., graduated)
  • Lakshminarayanarao Bachu - Water resources (M.Sc. candidate)
  • Nader Keshta - ater resources (Ph.D. candidate)
  • Kamban Parasuraman - Water resources(Ph.D. candidate)
  • Ajeet Kumar - Structures and Materials (M.Sc. candidate)
  • Kamal - Structures and Materials (Ph.D. candidate)
  • Sayeed Munshi - Structures and Materials(M.Sc. candidate)
  • Vijaya Nadide - Geotechnical (M.Sc. candidate)

Projects

Water resources

  • Modeling of Hydrological Processes Using Pattern Recognition and Soft-Computing Techniques
  • Performance Assessment and Hydrologic Modeling of Reconstructed watersheds and reclamation strategies
  • Risk-based Assessment of the Sustainability of the Reclamation Strategy
  • Multi-Criterion Decision Analysis (MCDA), Uncertainty, and Value of Data and Models in water resources engineering
  • Modeling of Hydrological Processes Using Pattern Recognition and Soft-Computing Techniques (Funded by NSERC)

Most of the hydrological processes vary both spatially and temporally and embedded with nonlinearity in spatial and temporal scales. The mechanistic models used to model such processes would require large amounts of high-quality data and a good understanding of the underlying physics to model the nonlinear relationships. Also, many mechanistic hydrologic models ignore patterns and thresholds, and assume that physical relationships hold over the entire range of hydrological conditions. They also ignore scaling issues - formulae developed at a local scale are used in watershed models at various spatial and temporal scales. The result is a complicated and ad-hoc model calibration process that accounts implicitly for the above-noted shortcomings.

Saskatoon Flood

A viable alternative to the mechanistic modeling approach is to make use of soft-computing techniques, such as Clustering Techniques (CT), Artificial Neural Networks (ANNs), Wavelet Networks (WNs), Wavelet Analysis (WA), Genetic Algorithms (GAs), and Genetic Programming (GP) to model the hydrological processes. These techniques map the inputs to outputs without the need to identify the physics a priori. The aim of this research project is to investigate the possibilities of employing pattern recognition and soft-computing techniques to build robust and parsimonious hydrologic models.

  • Performance Assessment and Hydrologic Modeling of Reconstructed watersheds and reclamation strategies (Funded by NSERC & Syncrude Canada Ltd.)

This research project aims at developing a framework to help understand the dynamics of the hydrologic processes that are dominant on the reconstructed watersheds as a result of different reclamation strategies. The overall goal of the intended framework is to help the oil sands mining industry as well as the regulators develop, adopt, and enforce a sustainable reclamation strategy. Such a strategy requires a comprehensive understanding of both short-term and long-term evolution of reclaimed watersheds. Since 1999, the oil sands mining industry has launched an extensive monitoring program installed on experimental covers in Northern Alberta, Canada. The sizable amount of meteorological, hydrological, hydrogeological, and ecological data collected, although apparently useful and desirable, can lead to paralysis of analysis in the absence of a framework that guides all stakeholders through the decision-making process.

The framework is founded on the ongoing program of extensive monitoring. Initial understanding is used to go through two parallel approaches of mechanistic and inductive (data-driven) modeling of the reconstructed watersheds as a not fully understood system. The outputs from the two different approaches will be used to encapsulate the initial understanding of the system within a prescriptive decision analysis (DA) approach that entails comprehensive and detailed sensitivity and uncertainty analyses. The DA approach along with the initial understanding of the system of reclaimed watersheds, which is fed with knowledge gained from comparison with natural systems, will be utilized to provide feedback to the monitoring program and the modeling exercise. Re-directed monitoring and refined modeling will help achieve the desired comprehensive understanding of the system of reclaimed watersheds. Finally, the system understanding can be quantified towards modifying existing regulations and reclamation practices to develop sustainable reclamation strategy (SRS).

  • Risk-based Assessment of the Sustainability of the Reclamation Strategy (Funded by CEMA)

To establish the maximum annual moisture deficit for different ecosites, the factors affecting the timing and magnitude of water balance components, particularly evapotranspiration, must be determined. This hydrological information can then be used to calibrate and test the system dynamics watershed (SDW) model (Elshorbagy et al., 2005), which upon validation is driven by long0term climate data to generate the frequency curves for assessing the probability of cover failure. However, the likelihood of cover failure will vary based upon cover design, vegetation species and age. Therefore, it is necessary to examine different covers at discrete stages of development and natural sites for a complete assessment of restoration strategies.

  • Multi-Criterion Decision Analysis (MCDA), Uncertainty, and Value of Data and Models in water resources engineering

Publications

Refereed Journal Articles & Book Chapters

  1. Elshorbagy, A. 2006. Accuracy and Uncertainty: A False Dichotomy in Engineering Education. A Case Study From Civil Engineering. International Journal of Engineering Education (accepted)
  2. Parasuraman, K., Elshorbagy, A. and Si, B. 2006. Estimating saturated hydraulic conductivity in spatially-variable fields using neural network ensembles. Soil Science Society of America Journal, 70: 1851-1859.
  3. Elshorbagy, A. and Barbour, S. L. 2006. Risk and uncertainty-based assessment of the hydrologic performance of reconstructed watersheds. Journal of Geotechnical and Geoenvironmental Engineering, ASCE (under review).
  4. Elshorbagy, A., and Parasuraman, K. 2006. Toward Bridging the Gap Between Data-driven and Mechanistic Models: Cluster-based Neural Networks for Hydrologic Processes. In: Abrahart, R., See, L., and Solomatine, D. (Eds.), Hydroinformatics in practice: computational intelligence and technologicaldevelopments in water applications(accepted).
  5. Parasuraman, K., and Elshorbagy, A. 2007. Cluster-Based Hydrologic Prediction Using Genetic-Algorithm-Trained Neural Networks. Journal of Hydrologic Engineering, ASCE, 12(1): (in press).
  6. Elshorbagy, A., Parasuraman, K., Putz, G., and Ormsbee, L. 2006. Deterministic and Probabilistic Approaches to the Development of pH Total Maximum Daily Loads: A Comparative Analysis, Journal of Hydroinformatics (accepted).
  7. Elshorbagy, A. 2006. Multi-criterion Decision Analysis Approach to Assess the Utility of Watershed Modeling for Management Decisions. Water Resources Research, 42, W09407, doi:10.1029/2005WR004264.
  8. Parasuraman, K.,Elshorbagy, A., and Carey, S.K. 2005. Spiking-Modular Neural Networks: A Neural Network Modeling Approach for Hydrological Processes. Water Resources Research, 42, W05412, doi:10.1029/2005WR004317.
  9. Elshorbagy, A., Barbour, L. and Qualizza, C. 2006. Chapter 14: Multi-criterion Decision Analysis Approach to Assess the Performance of Reconstructed Watersheds. In R. S. Sessa (Ed.), Topics on System Analysis and Integrated Water Resource Management (IWRM), Elsevier, The Netherlands, 257-269.
  10. Elshorbagy, A., Teegavarapu, R., Ormsbee, L. 2006. Assessment of Pathogen Pollution in Watersheds Using Object-Oriented Modeling and Probabilistic Analysis. Journal of Hydroinformatics, 8(1): 51-63.
  11. Elshorbagy, A., Ormsbee, L. 2006. Object-oriented modeling approach to surface water quality management. Environmental Modeling and Software, 21(5): 689-698.
  12. Elshorbagy, A. 2005. Learner-centered Approach to Teaching Watershed Hydrology Using System Dynamics. International Journal of Engineering Education, 21(6): 1203-1213.
  13. Elshorbagy, A., Jutla, A., Barbour, L., Kells, J. 2005. System Dynamics Approach to Assess the Sustainability of Reclamation of Disturbed Watersheds. Canadian Journal of Civil Engineering,35(1): 144-158.
  14. Elshorbagy, A., Teegavarapu, R. and Ormsbee, L. 2005. Total Maximum Daily Load (TMDL) Approach to Surface Water Quality Management: Concepts, Issues and Applications. Canadian Journal of Civil Engineering,35(2): 442-448.
  15. Teegavarapu, R. Elshorbagy, A. 2005. Fuzzy Set Based Error Measure for Hydrologic Model Evaluation. Journal of Hydroinformatics, 7(3): 199-208.
  16. Elshorbagy, A., Teegavarapu, R. and Ormsbee, L. 2005. Framework for Assessment of Relative Pollutant Loads in Streams in Data-poor Conditions. Water International, 30(4): 477-486.
  17. Ormsbee, L., Elshorbagy, A. and Zechman, E. 2004. A Methodology for pH TMDLs: Application to Beech Creek Watershed. Journal of Environmental Engineering, ASCE, 130(2): 167-174.

Conference Proceedings & Presentations:

  1. Elshorbagy, A. 2006. Uncertainty Analysis in Engineering Education: Bridging the Gap Between Theory and Practice. Proceedings of The 7th International Conference on Hydroinformatics HIC 2006, Nice, France, September 4 - 8, V4, 3127-3134.
  2. Parasuraman, K., Elshorbagy, A., and Carey, S. 2006.Genetic Programming as a Model Induction Engine for Characterizing The Evapotranspiration Process. Proceedings of The 7th International Conference on Hydroinformatics HIC 2006, Nice, France, September 4 - 8, V2, 815-822.
  3. Elshorbagy, A. and Barbour, S.L. 2006. Probabilistic Assessment of the Sustainability of Restored Watersheds. Proceedings of The 7th International Conference on Hydroinformatics HIC 2006, Nice, France, September 4 - 8, V4, 3039-3046.
  4. Kelln, C. J.,Barbour, S. L., Elshorbagy, A., and Qualizza, C. 2005.Long-term Performance of a Reclamation Cover: The Evolution of Hydraulic Properties and Hydrologic Response. Unsaturated Soils Conference, Arizona, U.S.A., April 2 - 6.
  5. Jutla, A., Elshorbagy, A., and Kells, J. 2005.Beyond Rainfall-Runoff Modeling: Hydrologic simulation of Reconstructed Watersheds Using System Dynamics. 17th Canadian Hydrotechnical Conference, Edmonton, AB, Canada, August 17-19.
  6. Elshorbagy, A. 2005. Predicting the Uncertainty of Watershed Models Using a Simple Bayesian Approach. 17th Canadian Hydrotechnical Conference, Edmonton, AB, Canada, August 17-19.
  7. Parasuraman, K. and Elshorbagy, A. 2005. Wavelet Networks: An Alternative to Neural Networks. International Joint Conference on Neural Networks, Montreal, QC, Canada, July 31-Aug 4 (poster).
  8. Parasuraman, K. and Elshorbagy, A. 2005. Modeling the Dynamics of Evaporation by Recurrent Artificial Neural Networks. 58th Annual CWRA NationalConference, Banff, AB, Canada, June 15-18 (poster).
  9. Parasuraman, K. and Elshorbagy, A. 2005. Cluster-based streamflow prediction using genetic algorithm-trained neural networks. General Assembly of the European Geosciences Union , Vienna, Austria, April 23-29 (poster & oral presentation).
  10. Elshorbagy, A. 2004. Multi-criterion Decision Analysis Approach to Assess the Performance of Reconstructed Watersheds. IFAC Workshop on Modelling and Control for Participatory Planning and Managing Water Systems. Sept 29-Oct 1, Venice, Italy.
  11. Elshorbagy, A. and Ormsbee, L. 2004. Water quality management using the TMDL approach: application in southern Kentucky. Proceedings of the Annual Conference of Canadian Society of Civil Engineers, Saskatoon , Saskatchewan , June 2-5.
  12. Parasuraman, K. and Elshorbagy, A. 2004. Performance of various heuristic methods in estimation of parameters for model calibration. Proceedings of the Annual Conference of Canadian Society of Civil Engineers, Saskatoon , Saskatchewan , June 2-5.
  13. Jutla, A.,Elshorbagy, A. and Kells, J. 2004. Predicting spring runoff in the Canadian Prairies using artificial neural networks. Proceedings of the Annual Conference of Canadian Society of Civil Engineers, Saskatoon , Saskatchewan , June 2-5.
  14. Alabi, P., Kells, J. and Elshorbagy, A. 2004. Use of artificial neural networks in describing complex flow field conditions. Proceedings of the Annual Conference of Canadian Society of Civil Engineers, Saskatoon , Saskatchewan , June 2-5.
  15. Azinfar, H., Kells, J. and Elshorbagy, A. 2004. Use of neural networks in the prediction of local scour below a sluice gate. Proceedings of the Annual Conference of Canadian Society of Civil Engineers, Saskatoon , Saskatchewan , June 2-5.

Technical Reports:

  1. Jutla, A., Elshorbagy, A. and Kells, J. 2006. Simulation of the hydrological processes on reconstructed watersheds using system dynamics. CANSIM Series Report No. CAN-06-01, Centre for Advanced Numerical simulation (CANSIM), Department of Civil & Geological Engineering, University of Saskatchewan, Saskatoon, SK, Canada, pp. 139.
  2. Elshorbagy, A. 2006. Performance assessment of hydrologic models based on the uncertainty of measurements. CANSIM Series Report No. CAN-06-02, Centre for Advanced Numerical simulation (CANSIM), Department of Civil & Geological Engineering, University of Saskatchewan, Saskatoon, SK, Canada, pp. 21.
  3. Elshorbagy, A. and Jutla, A. 2006. Tracing the evolution of reconstructed watersheds using the parameters of the system dynamics watershed model. CANSIM Series Report No. CAN-06-03, Centre for Advanced Numerical simulation (CANSIM), Department of Civil & Geological Engineering, University of Saskatchewan, Saskatoon, SK, Canada, pp. 32.
  4. Keshta, N.and Elshorbagy, A. 2006. Wetland hydrology: A literature review. CANSIM Series Report No. CAN-06-04, Centre for Advanced Numerical simulation (CANSIM), Department of Civil & Geological Engineering, University of Saskatchewan, Saskatoon, SK, Canada, pp. 33.

Contact

For questions or more information about CANSIM, please mail us at:

CANSIM, room 2C22, Eng. Bldg,
Dept. of Civil & Geological Eng., University of Saskatchewan
57 Campus Drive, Saskatoon, SK, Canada S7N 5A9
Phone: (306)966-5377
Fax: (306)966-5427

Or contact one of the following:

Prof. Amin Elshorbagy (Water resources) (306) 966-5414
Prof. Mohamed Boulfiza (Structures) (306) 966-5299