Sparse Stochastic Libraries for Spatially Correlated Hazard Modeling
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Solution Overview
Problem
Existing hazard simulation methods fail to accurately convey spatial correlations and risks, leading to erroneous results and impractical storage requirements, especially in large-scale scenarios like wildfire and infrastructure protection, due to the reliance on averages and lack of consideration for interrelationships between assets.
Innovation Solution
The use of Sparse Monte Carlo and event Polygons to model hazard intensity distributions, preserving spatial correlations, reduces storage requirements and enables practical decision-making by non-technical stakeholders through interactive simulations in Excel, utilizing Sparse Stochastic Libraries to disaggregate monolithic models into separate sub-models for hazards and assets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional hazard simulation methods are used to convey full distribution of hazard intensity, then measurement precision is improved, but storage requirements increase impractically
Solution Approach 1:
The patent segments the monolithic hazard simulation model into separate sub-models: hazard sub-models (generating hazard parameters) and asset sub-models (evaluating asset vulnerability). This segmentation allows the hazard distribution to be modeled with high precision while storing only essential hazard parameters rather than complete simulation data for all assets, dramatically reducing storage requirements while maintaining measurement precision for hazard intensity distribution.
2Reliability
If spatial correlations between assets are preserved in hazard simulations, then reliability of risk assessment is improved, but device complexity increases
Solution Approach 1:
The patent introduces an intermediary approach where hazard sub-models generate hazard parameters that inherently capture spatial correlations, and these parameters are then used by asset sub-models to evaluate risks. This intermediary hazard parameter generation process preserves spatial correlations and reliability of risk assessment without requiring complex interconnections between all asset models, thereby managing device complexity while maintaining assessment accuracy.
3Measurement precision
If monolithic hazard simulation models are used to account for all assets and hazards, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The patent divides the monolithic model into separate hazard and asset sub-models that can be independently developed, validated, and operated. This segmentation allows non-technical stakeholders to interact with simplified asset sub-models while the hazard sub-models handle complex hazard simulations in the background, maintaining measurement precision while improving ease of operation and decision-making accessibility.
Solution Approach 2:
The patent creates simplified representations (copies) of hazard scenarios through hazard parameters that capture essential characteristics without requiring stakeholders to interact with the full complexity of the monolithic simulation model. These hazard parameter copies enable non-technical users to perform risk assessments accurately without needing to understand or operate the complete simulation system.
Data Source
AI summary
A method according to an embodiment conveys the full distribution of hazard intensity while preserving spatial correlations, including the chances of simultaneous risk events. Sparse Monte Carlo and event Polygons reduce storage requirements by orders of magnitude compared to Pixel based approaches.