Abstract. Networks such as
transportation, water, and power are critical lifelines for society. Managers
plan and execute interventions to guarantee the operational state of their
networks under various circumstances, including after the occurrence of
(natural) hazard events. Creating an intervention program demands knowing the
probable direct and indirect consequences (i.e., risk) of the various hazard
events that could occur in order to be able to mitigate their effects. This paper
introduces a methodology to support network managers in the quantification of
the risk related to their networks. The methodology is centered on the
integration of the spatial and temporal attributes of the events that need to
be modeled to estimate the risk. Furthermore, the methodology supports the
inclusion of the uncertainty of these events and the propagation of these
uncertainties throughout the risk modeling. The methodology is implemented
through a modular simulation engine that supports the updating and swapping
of models according to the needs of network managers. This work demonstrates
the usefulness of the methodology and simulation engine through an
application to estimate the potential impact of floods and mudflows on a road
network located in Switzerland. The application includes the modeling of
(i) multiple time-varying hazard events; (ii) their physical and functional
effects on network objects (i.e., bridges and road sections); (iii) the
functional interrelationships of the affected objects; (iv) the resulting
probable consequences in terms of expected costs of restoration, cost of
traffic changes, and duration of network disruption; and (v) the restoration
of the network.
Emerging methodologies for natural hazard risk assessments involve the execution of a multitude of different interacting simulation models that produce vast amounts of spatio-temporal datasets. This data pool is further enlarged when such simulation results are post-processed using GIS operations, for example to derive information for decisionmaking. The novel approach presented in this paper makes use of the GPU-accelerated rendering pipeline to perform such operations on-the-fly without storing any results on secondary memory and thus saving large amounts of storage space. Particularly, algorithms for three frequently used geospatial analysis methods are provided, namely for the computation of difference maps using map algebra and overlay operations, distance maps and buffers as examples for proximity analyses as well as kernel density estimation and inverse distance weighting as examples for statistical surfaces. In addition, a visualization tool is presented that integrates these methods using a node-based data flow architecture. The application of this visualization tool to the results of a real-world risk assessment methodology used in civil engineering shows that the memory footprint of post-processing datasets can be reduced at the order of terabytes. Although the technique has several limitations, most notably the reduced interoperability with conventional analysis tools, it can be beneficial for other use cases. When integrated into desktop GIS applications, for example, it can be used to quickly generate a preview of the results of complex analysis chains or it can reduce the amount of data to be transferred to web or mobile GIS applications.
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