Difference between revisions of "Predictive scenarios"

From Opasnet
Jump to: navigation, search
(Created page with "{{encyclopedia|moderator=Aino paakkinen}} category:IEHIAS Table here Predictive scenarios give a relatively detailed and quanti...")
 
m
Line 1: Line 1:
{{encyclopedia|moderator=Aino paakkinen}}
+
[[Category:IEHIAS]]
[[category:IEHIAS]]
+
:''The text on this page is taken from an equivalent page of the [[Talk:IEHIAS|IEHIAS]]-project.
  
 
Table [[Formulating scenarios#Types of scenario| here]]
 
Table [[Formulating scenarios#Types of scenario| here]]

Revision as of 10:08, 15 August 2014

The text on this page is taken from an equivalent page of the IEHIAS-project.

Table here

Predictive scenarios give a relatively detailed and quantitative indication of how the system will change under a set of assumptions. Often, this is based on a statistical extrapolation of trends, or some form of deterministic model of reality. As such, the scenarios are exact, and provide no indication of uncertainties (though alternative, fixed predictions - e.g. of best and worst cases - may be defined to indicate the potential spread of outcomes).

Depending on the purpose of the scenario, and the methods used, the information may relate to many different aspects of the causal chain; in many cases, however, the focus tends to be on predicting either ambient environmental conditions or human exposures.

Examples might thus be:

  • air pollution: a detailed set of maps of predicted ambient air pollution concentrations across the area of interest;
  • waste management: a set of detailed predictions about the amounts of waste generated and proportions disposed via different routes;
  • road transport: detailed predictions of road traffic volumes and fleet mix, modal splits and passenger numbers.

References