A web-based software system for model integration in impact assessments of agricultural and environmental policies
The SEAMLESS consortium develops a computerized and integrated framework (SEAMLESS-IF) to assess the impacts on environmental and economic sustainability of a wide range of policies and technological improvements across a number of scales. In SEAMLESS-IF, different type of models are linked into model chains, where each model uses the outputs of another model as its inputs and ultimately indicators are calculated. This type of integrated modelling requires interoperability, which is the ability of two or more systems or components to exchange information and to use the information that has been exchanged. In SEAMLESS, we have developed an ontology to establish a set of shared domain concepts. To support a semantic-aware approach to model integration, all the commonly shared data types in SEAMLESS are declared in the ontology (starting from projects, describing the elements of an impact assessment study, down to the fine detail of the variables exchanged among the models). This is an important shift in the common approach to modelling: modellers specify the data requirements of their models on a higher level, i.e. that of an ontology, which is automatically transformed into a relational database model, to which ``data collecting’’ activities need to comply with. SEAMLESS-IF is based on a layered, client-server architecture. The end user interacts with the system by means of two web-based Graphical User Interfaces (GUI) that run as clients. The server-client architecture of SEAMLESS-IF allows for future applications to be developed and linked to the existing server, in order to cater for specific needs of different user groups.
J.-E. Wien, A. E. Rizzoli, R. Knapen, I. N. Athanasiadis, S. Janssen, L. Ruinelli, F. Villa, M. Svensson, P. Wallman, B. Jonsson, M. van Ittersum, A web-based software system for model integration in impact assessments of agricultural and environmental policies, Environmental and agricultural modelling: integrated approaches for policy impact assessment, pg. 207-234, 2010, Springer, doi:10.1007/978-90-481-3619-3_9.
You might also enjoy (View all publications)
- Mixing process-based and data-driven approaches in yield prediction
- Combining telecom data with heterogeneous data sources for traffic and emission assessments - an agent-based approach
- A weakly supervised framework for high-resolution crop yield forecasts