Workshops

Instructor: Hossein Bonakdari, PhD, P.Eng.
Department of Soil and Agri-Food Engineering, Laval University, Quebec, Canada

Date: Sunday 14 june 2020, 9 a.m. to 4 p.m.
Location: TBD
Language: This workshop will be in english
Cost: see Registration
 

Workshop description:

Artificial intelligence (AI) techniques and machine learning approaches will revolutionize many aspects of future agriculture engineering field. AI can be used as a promising tool to tackle different problems but related aspects of agricultural practical cases as great concern all over the world. The main focus of this course is to understand and discuss the recent developments in AI applications relating to practical engineering application. This course introduces a variety of different topics in AI approaches and learning methods in modeling and prediction of complex datasets. All codes are user friendly and trainees after this course will be able to use them for their cases. 

Course Overview

This introductory one day workshop covers key topics in Artificial Intelligence application in Agricultural Engineering:

  1. Data acquisition/Preprocessing:
    • outliers detection,
    • transferring raw information into usable data,
    • splitting the data into training & testing sets
  2. Classification:
    • decision tree,
    • support vector machine.
  3. Modeling tools:
    • Multilinear Regression (MLR)
    • Multi-layer perceptrons (MLP)
    • Adaptive Network-based Fuzzy Inference System (ANFIS),
    • Extreme Learning Machines (ELM),
  4. Post processing:
    • analysis of statistical indices,
    • scatter plot

Following completion of this course, trainee should

  • have an understanding of major AI techniques,
  • have a working knowledge of how to apply AI technologies to real-world datasets,
  • have gained experience designing and applying AI techniques in Agricultural Engineering practical problems

Who can attend?

Anyone who wants to learn about practical application of machine learning techniques can attend the workshop. No knowledge about programming is required.

Please note that the participants should bring their own laptops with minimum specifications as described in following section.

Technical requirements:

Participants are requested to bring their own laptops with MATLAB software.

About the instructor

BonakdariHossein Bonakdari, Ph.D, P.Eng., earned his Ph.D in Civil Engineering at the University of Caen-France. He has worked for several organizations like most recently as faculty member of department of Soils and Agri-Food Engineering at Laval University, Quebec. He has supervised several PhD and MSc students with teaching experience of more than 12 years in field of Artificial Intelligence application in practical Engineering applications. His fields of specialization and interest include: practical application of soft computing techniques in engineering problems. Results obtained from his researches have been published in more than 180 papers in international journals (h-index=26). He has also more than 150 presentations in national and international conference. He published two books. Dr. Bonakdari is currently leading several research projects in collaboration with industrials partners.

For more information, please contact This email address is being protected from spambots. You need JavaScript enabled to view it.

Instructor: Dr. Prasad Daggupati
School of Engineering, University of Guelph, Canada

Date: Sunday 14 june 2020, 9 a.m. to 4 p.m.
Location: TBD
Language: This workshop will be in english
Cost: see Registration

Workshop description:

The Soil & Water Assessment Tool (SWAT) is a basin scale hydrological model developed to quantify the impacts of different land management practices in large, complex watersheds. SWAT is a public domain model and is actively supported by USDA Agricultural Research Service, and Texas A& M University, USA. The model accounts multiple hydrological and water quality components such as: weather, surface runoff, return flow, percolation, evapo-transpiration, transmission losses, pond and reservoir storage, crop growth and irrigation, groundwater flow, reach routing, nutrient and pesticide loading, and water transfer. The various application of SWAT has been noted throughout the globe, as is evident by the number of peer-reviewed articles till 2019 (>3500).

The workshop is expected to provide an overview (approx. 20%) and a hands-on session (approx. 80%) of state-of-the-art SWAT (SWAT2012) model to academicians and practitioners in hydrology, watershed management, water resources engineering and agricultural water quality management. In this workshop, we will be demonstrating QSWAT which is a QGIS (open source, publicly accessible) plugin that enables us to run SWAT through a GIS desktop/ interface

What will be delivered?

  • An overview of hydrological models (Theory)
  • An overview on the various applications of SWAT (Theory)
  • Introduction to QSWAT interface (Hands-On/Theory)
  • Watershed delineation using Q-SWAT (Hands-On)
  • Landuse, soil overlay and delineate Hydrological Response Units (HRUs) (Hands-On)
  • Weather/weather generator and remaining inputs (including point sources) to develop SWAT model (Hands-On)
  • SWAT simulations and saving results (Hands-On)
  • Visualization and interpretation of SWAT outputs (Hands-On)

Who can attend?

Anyone who wants to learn about SWAT model can attend the workshop. However, the prospective participants are expected to have a working knowledge (loading vector and raster maps, panning, zooming, selecting features) of QGIS. We will not have time to review basic concepts of QGIS. Please note that the participants should bring their own laptops with minimum specifications as described in following section.

Technical requirements:

Participants are requested to bring their own laptops (Windows 10 OS, Microsoft .Net Framework 3.5, at least 4GB RAM, 20GB free hard disk space, Adobe Reader, Microsoft Office with MS ACCESS. Please go the SWAT model website (https://swat.tamu.edu/software/qswat/) and install the following software before training:

  • QGIS (QGIS Brighton 2.6)
  • QSWAT 1.9
  • SWAT Editor 2012.10.18
  • SWAT-CUP 5.1.6.2

About the instructor

headshot daggupatiDr. Daggupati is an Assistant Professor (Water Resource Engineering) in the School of Engineering at the University of Guelph, Canada. He received his BS degree (2012) from the College of Agricultural Engineering in India, Masters (2007) and Ph.D. (2012) degrees from Kansas State University in USA. He worked as a Post-Doctoral Research Associate and Assistant Research Scientist at Texas A&M University, USA before joining the University of Guelph. Dr. Daggupati’s major contributions to research and practical applications are towards solving emerging water quality and quantity issues using GIS, hydrological modeling, machine learning, and field experimentation at various scales (e.g. field scale, watershed scale) across multiple countries around the world. 

For further questions/information, please reach to us via This email address is being protected from spambots. You need JavaScript enabled to view it..