Geospatial Climate Impact Function Generator

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Solution Overview

Problem

Current climate impact modeling platforms are limited in adaptability, rely on qualitative definitions that can lead to biased evaluations, lack transparency, and fail to leverage geospatial context, resulting in inaccurate risk assessments for unprecedented climate hazards and assets.

Innovation Solution

A method that generates impact functions for geospatial climate hazards using user interactions, involving the creation of entity and universal knowledge graphs, graph neural networks, and symbolic regression to produce accurate and adaptable impact functions, incorporating data from user inputs, historical observations, and geospatial context.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If traditional climate impact modeling platforms are used, then the modeling process is simpler, but the adaptability to different climate hazards and assets is limited

Engineering Contradiction:

ImproveadaptabilityVSAvoidmodeling complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system dynamically adapts to different climate hazards and assets by automatically generating customized impact functions through symbolic regression, rather than using static predefined functions. The modeling platform adjusts its behavior based on the specific characteristics of the input data, hazard type, and asset being evaluated.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system changes the parameters and structure of impact functions automatically through symbolic regression analysis, transforming fixed predefined parameters into dynamically optimized parameters that fit the specific climate hazard and asset combination, thereby improving adaptability without manual reconfiguration.

Inventive Principle:

Principle #35Parameter changes

2Measurement precision

If qualitative definitions are used for impact functions, then the modeling process is easier, but the evaluation accuracy decreases due to bias

Engineering Contradiction:

Improveevaluation accuracyVSAvoidmodeling complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system replaces manual qualitative definition processes with automated symbolic regression analysis, substituting human judgment and qualitative assessments with computational algorithms that objectively derive impact functions from data, thereby eliminating bias and improving measurement precision.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system performs self-service by automatically generating impact functions through symbolic regression without requiring manual qualitative definitions or expert intervention, allowing the model to self-optimize its parameters and structures based on the input data characteristics.

Inventive Principle:

Principle #25Self-service

3Productivity

If manual impact function generation is used, then the model is more interpretable, but the productivity and automation level are low

Engineering Contradiction:

Improvegeneration efficiencyVSAvoidautomation level

Core Design Contradiction:

ProductivityVSExtent of automation

Solution Approach 1:

The system achieves high automation through self-service symbolic regression analysis, where the model automatically generates impact functions without manual intervention, significantly improving productivity while maintaining full automation capability through algorithmic self-optimization.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system extracts the essential relationships between climate hazards and assets through symbolic regression, separating the core impact function generation from manual processes, thereby automating the most complex and time-consuming aspects while maintaining interpretability of the extracted relationships.

Inventive Principle:

Principle #2Taking out (Extraction)

4Loss of information

If traditional modeling approaches are used, then the computational resources required are less, but the ability to leverage geospatial context is insufficient

Engineering Contradiction:

Improvegeospatial context utilizationVSAvoidcomputational resources

Core Design Contradiction:

Loss of informationVSUse of energy by moving object

Solution Approach 1:

The system performs preliminary symbolic regression analysis to identify and extract relevant geospatial context features before main modeling, pre-processing the geospatial data to capture essential relationships, thereby reducing information loss while optimizing computational resource usage by focusing on the most critical features.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20250103852A1Impact function generator for geospatial climate hazards

Publication Date: 2025.03.27 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

An embodiment for generating impact functions for geospatial climate hazards based on user interactions. The embodiment may receive input data associated with a target geospatial climate hazard and a corresponding asset, the input data including one or more of a first dataset corresponding to a predetermined set of prompts, and a second dataset corresponding to a natural language exchange. The embodiment may generate, based on the first dataset an entity knowledge graph including a series of candidate variables. The embodiment may generate, based on the second dataset, a universal knowledge graph including a series of candidate function formulas. The embodiment may generate, using a graph neural network, embeddings corresponding to the entity knowledge graph and the universal knowledge graph respectively. The embodiment may perform symbolic regression, using the embeddings, to generate one or more impact functions for the target geospatial climate hazard and the corresponding asset.