A collaborative virtual geographic environment for emergency dam-break simulation and risk analysis

Abstract

Since barrier lakes are affected by a highly changing and complex environment, it is always a great challenge to determine the proper actions that must be performed within a limited amount of time. Existing methods have some restrictions, such as the dispersed data resources and personnel, the loose model integration, and the complicated and unfriendly user interface, which results in low efficiency of dam-break risk assessment under emergency response conditions. These issues were addressed in this paper by constructing a collaborative virtual geographic environment (CVGE), which supports three levels of deeper applications including sharing of distributed resources, tightly geographic process simulation and visualisation analysis, and geographic collaboration. A virtual resource environment and mobile computing service were designed for the distributed resource sharing. A spatiotemporal process model based on cellular automata and parallel computing optimisation was proposed and integrated for real-time simulation and analysis of dam-break risk assessment of barrier lakes. A distributed virtual geographic scene and collaborative workflow were designed to improve the operational efficiency of the models with respect to initiation, computation, output visualisation and analysis. A prototype system was developed to support collaborative dam-break risk assessment of the Xiaojiaqiao barrier lake in Anxian County, Sichuan Province, China. The experimental results indicate that the proposed method could efficiently improve the efficiency of dam-break risk assessment on data supporting, model computation, and visualisation analysis.

Keywords::

• collaborative virtual geographic environment (CVGE)
• Distributed resource sharing
• real-time dam-break flood simulation
• visualisation analysis
• geographic collaboration

Acknowledgements

This research is partially supported by the National Key Basic Research Program of China (Grant No. 2015CB954101), the National Natural Science Foundation of China (Grant No. 41271389 and 41001252), the Program for Changjiang Scholars and Innovative Research Team in University (Grant No. IRT13092), Special Fund by Surveying & Mapping and Geoinformation Research in the Public Interest (201412010) and Open Research Fund by Sichuan Engineering Research Center for Emergency Mapping & Disaster Reduction (J2014ZC17). The authors thank two anonymous reviewers and editors whose comments have notably improved the manuscript.