Event Title

Enhancing Computational Hydrologic Modeling through Procedural and Semantic Modeling

Presenter Information

Aaron Byrd

Location

ECC 216

Event Website

http://water.usu.edu

Start Date

4-3-2012 4:00 PM

End Date

4-3-2012 4:05 PM

Description

Semantic modeling promises significant advances in automated reasoning to facilitate many analyses. Hydrologic modeling involves a significant amount of analysis and as such semantic modeling would seem to offer advantages. Semantic models are models of concepts and the relationships between them. These concepts form a web or graph of concepts. Semantic reasoning engines, which operate on these concept graphs to deduce knowledge and answer queries, have significant potential to facilitate automated analyses. We have developed a new form of reasoning engine and knowledge base that extends the general-purpose analysis and problem solving capability of reasoning engines by incorporating procedural knowledge, represented as computer source code, into the knowledge base. The source code, rather than be external to the knowledge base, is included as procedural aspects of the concepts included in the knowledge base. The reasoning engine is able to compile the code and then, if need be, execute the procedural code as part of a query. The potential advantage to this approach is that it simplifies the description of procedural knowledge in a form that can be readily utilized by the reasoning engine to answer a query. Further, since the form of representation of the procedural knowledge is source code, the procedural knowledge has the full capabilities of the underlying language. The process of hydrologic modeling utilizes conceptual and procedural analyses to transform data and create models. While procedural analyses, in the form of software, are commonly used by hydrologists, semantic analyses are not yet widely used. This work examines the utility of integrated semantic and procedural hydrologic knowledge models. We have created semantic and procedural models to facilitate watershed delineation and model integration analyses. The demonstration examples presented show how integrated procedural and semantic models of the tasks and concepts that surround our computational hydrologic models and tools can facilitate and automate their usage as well as simplify the integration of their results with other models and tools. The results show that integrating procedural and semantic knowledge models into how we work as hydrologists could greatly facilitate model identification and development, and by extension, the scientific advancement of the field of hydrology.

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Apr 3rd, 4:00 PM Apr 3rd, 4:05 PM

Enhancing Computational Hydrologic Modeling through Procedural and Semantic Modeling

ECC 216

Semantic modeling promises significant advances in automated reasoning to facilitate many analyses. Hydrologic modeling involves a significant amount of analysis and as such semantic modeling would seem to offer advantages. Semantic models are models of concepts and the relationships between them. These concepts form a web or graph of concepts. Semantic reasoning engines, which operate on these concept graphs to deduce knowledge and answer queries, have significant potential to facilitate automated analyses. We have developed a new form of reasoning engine and knowledge base that extends the general-purpose analysis and problem solving capability of reasoning engines by incorporating procedural knowledge, represented as computer source code, into the knowledge base. The source code, rather than be external to the knowledge base, is included as procedural aspects of the concepts included in the knowledge base. The reasoning engine is able to compile the code and then, if need be, execute the procedural code as part of a query. The potential advantage to this approach is that it simplifies the description of procedural knowledge in a form that can be readily utilized by the reasoning engine to answer a query. Further, since the form of representation of the procedural knowledge is source code, the procedural knowledge has the full capabilities of the underlying language. The process of hydrologic modeling utilizes conceptual and procedural analyses to transform data and create models. While procedural analyses, in the form of software, are commonly used by hydrologists, semantic analyses are not yet widely used. This work examines the utility of integrated semantic and procedural hydrologic knowledge models. We have created semantic and procedural models to facilitate watershed delineation and model integration analyses. The demonstration examples presented show how integrated procedural and semantic models of the tasks and concepts that surround our computational hydrologic models and tools can facilitate and automate their usage as well as simplify the integration of their results with other models and tools. The results show that integrating procedural and semantic knowledge models into how we work as hydrologists could greatly facilitate model identification and development, and by extension, the scientific advancement of the field of hydrology.

https://digitalcommons.usu.edu/runoff/2012/Posters/24