Hazard Forecasting System Grid Resolution and Risk Assessment
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Solution Overview
Problem
Existing systems for forecasting and assessing hazard-related effects, such as floods and wildfires, lack the resolution to accurately associate hazards with specific locations, leading to inadequate risk assessment and financial loss estimation, especially in the context of changing climate conditions.
Innovation Solution
A system that provides hazard-related information at a fine resolution, associating hazard severity values with specific locations and building footprints, using probabilistic models to predict likelihoods and estimate financial losses, while considering uncertainties and changing environmental factors like sea level rise and precipitation patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing hazard forecasting systems are used, then general hazard information is provided, but the resolution is insufficient to accurately associate hazards with specific locations and properties
Solution Approach 1:
The system segments hazard assessment into discrete grid cells (e.g., 30m x 30m pixels) and individually processes each cell through probabilistic models. This segmentation enables location-specific hazard predictions while maintaining computational efficiency through parallel processing of grid cells, resolving the contradiction between precision and complexity.
Solution Approach 2:
The system transitions from traditional coarse-resolution hazard maps to a multi-dimensional framework that incorporates fine spatial resolution (grid cells), temporal dimensions (future time periods), and probabilistic dimensions (likelihood ranges). This dimensional expansion enables precise location association while structuring the complexity in a manageable framework.
2Loss of information
If fine resolution hazard information is provided for specific locations, then accurate risk assessment is enabled, but data processing and computation complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-processing climate model outputs and hazard parameters into standardized formats before main analysis. Grid cells are pre-characterized with their physical properties, and probabilistic models are pre-calibrated, reducing computation complexity during actual hazard assessment while maintaining high information accuracy.
Solution Approach 2:
The system uses representative grid cells and probabilistic model templates that can be copied and applied across different locations. Instead of developing unique models for each property, standardized templates are replicated and parameterized for specific locations, reducing data processing complexity while preserving location-specific accuracy.
3Reliability
If probabilistic models are used to predict hazard likelihoods and financial losses, then comprehensive risk assessment is achieved, but computational requirements and processing time increase
Solution Approach 1:
The system implements partial action by calculating hazard likelihoods for selected probability thresholds (e.g., 10%, 50%, 90% likelihoods) rather than continuous probability distributions. This selective computation provides sufficient reliability for risk assessment while significantly reducing processing time compared to exhaustive probabilistic analysis.
Solution Approach 2:
The system uses periodic action by updating hazard assessments at predetermined time intervals (e.g., annual updates or when new climate data becomes available) rather than continuous real-time processing. This approach maintains reliable risk assessment while managing computational requirements through scheduled batch processing.
4Adaptability or versatility
If climate change impacts are incorporated into hazard forecasts, then future risk prediction accuracy is improved, but model complexity and data requirements increase
Solution Approach 1:
The system achieves universality by designing a multi-functional probabilistic model framework that handles multiple hazard types (floods, wildfires, storms), multiple climate scenarios, and multiple time periods within a single unified model structure. This universal framework provides climate change adaptability while avoiding the complexity of separate specialized models for each hazard type.
Solution Approach 2:
The system manages model complexity through parameter changes by adjusting key parameters (e.g., climate sensitivity factors, emission scenarios) rather than restructuring the entire model. This allows the system to adapt to different climate change projections by modifying parameters while maintaining the core model architecture, balancing adaptability with computational tractability.
Data Source
AI summary
Hazard-resultant effects to land and buildings are predicted based on various inputs. Hazards may include any appropriate type of hazard (e.g., flood, wildfire, climate-related hazards, or the like). Inputs may include the likelihood that that a specific type of hazard may occur for various scenarios, terrestrial boundaries, property boundaries, census geographies, or the like. Relationships between the inputs are determined and used to quantify parameters pertaining to a specific type of hazard. For example, the depth of flood water may be predicted for a particular terrestrial boundary, a city or town, or a building, for specific climate scenarios. A risk likelihood of the quantified parameter may be determined for a particular period of time and environment. For example, flooding to a building may be determined, broken down by depth threshold and year of annual risk for specific climate scenarios. Economic loss also may be predicted.