Publication details
The Machine-Learning Methods in the Environmental Risk Assessment Spatial Modelling
| Authors | |
|---|---|
| Year of publication | 2006 |
| Type | Article in Proceedings |
| Conference | Proceedings of the 2nd international summer school on computational biology |
| MU Faculty or unit | |
| Citation | |
| Field | Informatics |
| Keywords | machine learning; spatial modeling; environmental risk assessment |
| Description | Combining GIS, ERA, and ML is a very promising and challenging but also demanding issue. Even if the models for small areas appear simpler (more transparent), it is not always true. In many cases these small areas (water streams without surrounding areas, only tillages without cities or factories) are affected by processes which are detectable only in a larger scale. In the small areas, these processes usually look like some noise or even some unpredictable events. Needless to say, the more global areas or models covering more areas of interest always accumulate complex problems. In these larger areas, different problems, hardly covered by the small models, occur. The scientific effort is now focused on moving of various pollutants (in water, soil, air). These movings are again affected by the various direct and indirect processes which are hardly possible to describe. |
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