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8 - Data Mining, Machine Learning and Spatial Data Infrastructures for Scenario Modelling

Published online by Cambridge University Press:  13 March 2020

By
Neil Sang  and
Matthew Aitkenhead
Edited by
Neil Sang
Neil Sang
Affiliation:
Swedish University of Agricultural Sciences
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Summary

The models discussed in other chapters in this book relate (generally) to some form of simulation or representation in a formal modelling language. The range of computational or technological complexity involved is variable, but in most cases a very high degree of domain knowledge is also required with respect to the system under investigation. This presupposes that such expertise is available, and indeed that it is sufficient to understand and represent a particular system. For large coupled systems with a wide range of socio-economic, ecological and biophysical systems interacting, this may not be feasible. As with nature itself, NBS are often part of a complex web of interdependent systems so this chapter explores data mining as a pragmatic alternative/complementary approach when systems are insufficiently well-described by current theory or where domain expertise is in short supply. Examples are provided in Table 8.1.

Type
Chapter
Information
Modelling Nature-based Solutions
Integrating Computational and Participatory Scenario Modelling for Environmental Management and Planning
 , pp. 276 - 304
Publisher: Cambridge University Press
Print publication year: 2020

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