Keywords
multi-criteria decision support, bayesian networks, non-point source pollution
Start Date
1-7-2004 12:00 AM
Abstract
Modelling systems frequently work in a single domain, such as physical or chemical process modelling, hydrology or combinations, to simulate process in nature such as pollution transport or the production of food or manufactured goods. Side-effects of agro-industrial processes, or gains / losses from production enterprises are separately modelled without the ability to examine trade-offs or alternatives. Multi-objective modeling attempts to combine "apples and oranges" through decision theoretical principles. Such treatments can couple production and waste systems to quantify the economic cost of remediation. We demonstrate such an application, from the data acquisition, model calibration through to the hypothesis testing, for a nonpoint source pollution model together with a yield / energy / revenue model based on corn / grain / meadow rotations typically found in Southern Ontario, Canada, using realistic economic data obtained from agricultural operations similar to those found in this region.
Possible Courses: Multi-Objective Modelling and Decision Support Using a Bayesian Network Approximation to a Nonpoint Source Pollution Model
Modelling systems frequently work in a single domain, such as physical or chemical process modelling, hydrology or combinations, to simulate process in nature such as pollution transport or the production of food or manufactured goods. Side-effects of agro-industrial processes, or gains / losses from production enterprises are separately modelled without the ability to examine trade-offs or alternatives. Multi-objective modeling attempts to combine "apples and oranges" through decision theoretical principles. Such treatments can couple production and waste systems to quantify the economic cost of remediation. We demonstrate such an application, from the data acquisition, model calibration through to the hypothesis testing, for a nonpoint source pollution model together with a yield / energy / revenue model based on corn / grain / meadow rotations typically found in Southern Ontario, Canada, using realistic economic data obtained from agricultural operations similar to those found in this region.
