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Proceedings Paper

Web-based collaborative decision support services for river runoff and flood risk prediction in the Oak Ridge Moraine Area, Canada
Author(s): Lei Wang; Qiuming Cheng
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Paper Abstract

River runoff is highly related to the precipitation events and the land use characteristics. It is an important component in the hydrologic cycle because of its relationship to issues such as flood and water quantity. The Oak Ridge Moraine (ORM) Area, Southern Ontario has always been faced with the impacts of extreme hydrological events. Flood not only has an impact on the ORM economical, social well-being and particularly public safety, but also exacerbates major environmental problems. Prediction of flood is a complex system of which involves variable factors including climate condition, basin attributes, land use/cover types and ground water discharge. The application of flood prediction model requires the efficient management of large spatial and temporal datasets, which involves data acquisition, storage, and processing, as well as manipulation, reporting and display results. The complexity of flood prediction makes it difficult for individual organization to deal effectively with decision-making. Difficulty in linking data, analysis tools and models across organization is one of the barriers to be overcome in developing integrated river runoff and flood risks prediction system. Therefore, it is required to develop a standardized framework for Web-based Collaborative Decision Support Services (WCDSS), supporting information exchange and knowledge and model sharing from different organizations on the web. Such a WCDSS supply both metadata services, geo-data services and geo-processing services to help collaborative decision-making, not only support distributed data sharing and services, but also support distributed model sharing and services. This paper develop a WCDSS that provides a comprehensive environment for on-line river runoff and flood risk prediction, integrating information retrieval, analysis and model analysis for information sharing and decision-making support. Such a SDSS will improve understanding of the environmental, planning and management issues and emergency management and response associated with the ORM's water environment, and to develop sustainable solutions.

Paper Details

Date Published: 28 October 2006
PDF: 8 pages
Proc. SPIE 6421, Geoinformatics 2006: Geospatial Information Technology, 64211N (28 October 2006); doi: 10.1117/12.713112
Show Author Affiliations
Lei Wang, York Univ. (Canada)
Qiuming Cheng, York Univ. (Canada)
China Univ. of Geosciences (China)


Published in SPIE Proceedings Vol. 6421:
Geoinformatics 2006: Geospatial Information Technology
Huayi Wu; Qing Zhu, Editor(s)

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