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NOVA course on modelling and simulation of ecosystem services and environmental resource management with artificial intelligence

  • Photo: 
    Illustrasjon: Shutterstock, Merkushev Vasiliy

NOVA PhD course of 5 ECTS, organised by Dr. Stefan Bäckman, University of Helsinki: Faculty of Agriculture and Forestry, Dept. of Economics & Management. 
Course dates and location: 3-7 Jun. 2019, in Helsinki, Finland.
Also live-streamed to those wo will attend the course elsewhere in the Nordic and Baltic area.

Are you working with methodological problems in ecosystem, environmental resource management or economic modeling? Are you missing good tools and techniques for data mining, optimization or nonlinear modelling? In this course you will learn how to apply cutting-edge AI and optimization methods to your research problems in the lectures held by the experts in these fields.

Course Description
This course focuses in a multidisciplinary manner on model constructions and simulations of various ecosystem service and environmental resource management applications by using the methods of artificial intelligence (AI). Concrete application areas include such modellings and simulations as CO2, NO2 and NH3 emissions, farming runoffs, precision farming and waste water management. The AI methods mainly comprise neural networks, fuzzy systems and probabilistic reasoning methods as well as novel evolutionary optimization methods. In this manner, even complex models or systems may be studied fluently in a computer environment.

These methods may still be quite uncommon and even unfamiliar in the Nordic and Baltic countries, in particular in the above-mentioned application areas. Hence, there is a justified need for this type of course that the Nordic and Baltic region scholars can better meet the challenges provided by the international scientific community and the business world. Today AI is also an important issue when the governments in the Nordic and Baltic areas aim to enhance their competitiveness in the future Global markets.

Content
This course will present the basic methods of AI and optimization for model construction and com-puter simulation. These methods are applied to ecosystem services and environmental resource man-agement by studying such concrete examples as modellings and simulations of CO2, NO2 and NH3 emissions, farming runoffs, precision farming and waste water management. The corresponding traditional mathematical and statistical methods are also considered to some extent. The participants have also possibility for personal supervision and guidance by the teachers concerning their own work during the course after the classes.

Programme Outline
In Helsinki the course is held in the Faculty of Agriculture and Forestry in a computer class in Viikki campus by principally using Matlab software (maybe also with R or Excel), but previous knowledge of this is not required. Basic knowledge on statistics is recommended.

The course materials and instructions for passing the course are given in digital form. The lectures are also live-streamed to the Nordic and Baltic areas with Adobe Connect for those who are not arriving to Viikki Campus.

The teachers:

  1. Laszlo Koczy (Széchenyi István University in Győr, Hungary) who is one of the leading scholars in AI, fuzzy cognitive maps and optimization.
  2. Kirsi Virrantaus (Professor of Geoinformatics, Dept. of Built Environment, Aalto University, Finland) who has a long career on studying such geoinformatics applications as data mining and pattern recognition.
  3. Docent Vesa A. Niskanen (University of Helsinki, Faculty of Agriculture and Forestry, Dept. of Economics & Management, Finland, also co-organizer) who has a long international career within AI, in particular in studies of fuzzy, neuro-fuzzy and genetic-fuzzy systems as well in fuzzy cognitive maps.
  • Niskanen: on Monday and Tuesday, June 3-4, at 10-15.
  • Virrantaus: on Wednesday, June 5, at 10-15.
  • Koczy: on Thursday and Friday, on June 6-7, at 10-15.

Pre-/Post-Campus Assignments
Learning material is provided prior to course.

Learning Outcomes
Knowledge: highly specialised, novel, cutting-edge and multidisciplinary knowledge on AI and optimization in ecosystem service and environmental resource management applications. Also, basic knowledge on using AI in economics 
Skills: specialized problem-solving skills for applying cutting-edge AI and optimization methods when constructing computer models and computer simulations in ecosystem services and environmental resource management.
Competence: ability to manage and transform novel AI and optimization modeling when working or studying with ecosystem service and environmental resource management applications especially when complex models are examined or novel strategic approaches are expected.

Evaluation Elements
For passing the course, a course report should be written (7-10 pages) in which an ecosystem service or environmental resource management application based on the foregoing methods is presented. These application ideas will be considered and discussed with the participants in the course, and their academic degrees are taken into account in this context. If passed the course, 5 credits will be given.

Pedagogical Approach
Lectures in computer class in Viikki Campus Helsinki and live-streamed for distance students.

Estimated Workload

  • 10 hrs independent work prior to course (material is given)
  • 115 hrs model construction and report writing.
  • 20 hrs of lectures (5 x 4 hrs) within one week from Monday to Friday.
  • Personal guidance of participants at least 5 hrs

Prerequisite Knowledge
Basic knowledge on statistics is recommended. 

Admission
Admission for NOVA courses is handled by the course organiser/ the NOVA member institution organising the course. Please see the links in the margin for more information.

Application is closed.
There are unfortunately no available seats on the course.

 

 

Costs
To be announced

Published 16. January 2019 - 12:55 - Updated 7. March 2019 - 10:08

NOVA University Network

E-mail:
secretariat@nova-university.org
 

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