AI Climate Risk Prediction Using Property Features
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Solution Overview
Problem
Current systems lack accurate methods for predicting the likelihood and extent of damage from climate events such as wildfires, floods, and severe storms to specific properties, failing to account for property-specific attributes effectively.
Innovation Solution
The development of AI-driven systems that generate user interfaces to assess climate event risks, utilizing climate models to provide incidence and damage scores based on property-specific features, including geographic and temporal data validation, and machine learning algorithms to predict the probability and severity of climate events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional climate event prediction methods are used, then the system is simple to operate, but the prediction accuracy and ability to account for property-specific attributes is insufficient
Solution Approach 1:
The patent replaces traditional mechanical/statistical prediction systems with an artificial intelligence-based system that uses machine learning models to analyze property-specific attributes and predict climate event risks. The AI system processes multiple data sources including satellite imagery, geographic data, and historical climate information to generate accurate predictions without requiring complex manual analysis.
Solution Approach 2:
The system changes the parameters used in prediction by incorporating numerous property-specific attributes such as building materials, roof type, landscaping features, and precise geographic coordinates. These parameter changes enable the system to differentiate between properties in the same geographic area, providing customized risk assessments rather than general area-wide predictions.
2Measurement precision
If AI-driven property-specific assessment is implemented, then the prediction accuracy improves, but the computational resources and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing property attribute data, satellite imagery, and historical climate information in structured databases before actual prediction is needed. This allows the AI model to quickly retrieve and analyze relevant data during prediction events, reducing real-time computational requirements while maintaining high accuracy.
Solution Approach 2:
The system extracts only the most relevant features and attributes from large datasets for each prediction task. Rather than processing all available data, the AI model identifies and utilizes key property-specific features that have the greatest impact on climate event risk, reducing computational overhead while preserving prediction accuracy.
3Measurement precision
If comprehensive property features are analyzed, then the differentiation between properties improves, but the data processing complexity increases
Solution Approach 1:
The patent segments the property assessment process into distinct modules: satellite imagery analysis, geographic data processing, building attribute evaluation, and climate risk calculation. Each module handles specific types of data and produces intermediate results that are integrated by the AI system. This segmentation reduces processing complexity by breaking down the comprehensive analysis into manageable, specialized components.
Solution Approach 2:
The system introduces intermediary processing layers that standardize and normalize diverse property data formats before analysis. These intermediaries convert different data sources (imagery, geographic data, building records) into a unified structure that the AI model can efficiently process, reducing the complexity of handling heterogeneous comprehensive property features.
Data Source
AI summary
The disclosure includes a system and method for generating, using one or more processors, a user interface, wherein the user interface includes: a first portion associated with a location of a property of interest input by a user; a second portion associated with an image of the property of interest; a third portion associated with a climate event incidence score representing a relative probability of a first type of climate event occurring at the property of interest; a fourth portion associated with a climate event damage score representing a relative severity of damage to the property of interest were the first type of climate event to occur at the property of interest; and sending, using one or more processors, the user interface for presentation to the user.