Home Hazard And Vulnerability Models Using Natural-Disaster Shed Data
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Solution Overview
Problem
Existing systems inefficiently utilize vast amounts of uncoordinated and unfiltered natural disaster data, leading to excessive consumption of compute, memory, and network resources, and fail to provide accurate real-time risk assessments for natural disasters.
Innovation Solution
Utilizing customized, filtered, and refined environment indicator data, including fire shed data, through machine learning models to generate precise hazard and vulnerability models, optimizing resource use and enabling efficient risk mitigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If vast amounts of uncoordinated and unfiltered natural disaster data are utilized, then comprehensive risk assessment coverage is improved, but compute, memory, and network resources are excessively consumed
Solution Approach 1:
The patent extracts and utilizes only the most relevant features from natural disaster data through machine learning models. The system identifies and processes key indicators such as fire shed data, weather conditions, and terrain features while discarding redundant information, thereby achieving accurate risk assessments with reduced computational resource consumption.
Solution Approach 2:
The patent transforms raw natural disaster data into optimized parameter representations through machine learning feature extraction. By converting unstructured data into standardized risk parameters and indicators, the system improves processing efficiency and reduces memory and network resource requirements while maintaining assessment accuracy.
2Productivity
If customized and filtered environment indicator data is used through machine learning models, then resource efficiency is improved, but data processing complexity increases
Solution Approach 1:
The patent performs preliminary filtering and organization of environment indicator data before processing. By pre-processing and structuring data in advance, the system reduces the complexity of subsequent machine learning operations while maintaining high resource efficiency in the main processing pipeline.
3Speed
If real-time risk assessments are provided, then responsiveness to natural disasters is improved, but computational load increases
Solution Approach 1:
The patent replaces traditional mechanical data processing methods with machine learning-based automated analysis. The ML models enable real-time risk assessment by automatically identifying patterns and predicting outcomes without requiring extensive computational power for manual analysis, thereby achieving speed without proportional increases in computational load.
Data Source
AI summary
Techniques for calculating or determining risk scores for certain natural disasters perils based on machine learning model outputs are discussed herein. For example, a machine learning model may weight each of the pixels of a map in accordance with the set of weights associated with a structure, to calculate a risk score for a particular natural disaster peril associated with that structure. A plurality of risk selections may be provided to a user computing device for selection by a user, with those risk selections being associated with that risk score. Advantageously, the computing system facilitates the interaction of datasets with different measurement parameters in a machine learning model. In normalizing datasets before providing the datasets to input nodes of a machine learning model, a computing system may efficiently provide hazard and vulnerability outputs of the machine learning model.