Impact function generator for geospatial climate hazards

1 . A computer-based method for generating impact functions for geospatial climate hazards based on user interactions, the method comprising:

receiving input data associated with a target geospatial climate hazard and a corresponding asset, the input data comprising one or more of a first dataset corresponding to a predetermined set of prompts, and a second dataset corresponding to a natural language exchange;

generating, based on the first dataset an entity knowledge graph including a series of candidate variables related to the target geospatial climate hazard and the corresponding asset;

generating, based on the second dataset, a universal knowledge graph including a series of candidate function formulas that are most associated with the target geospatial climate hazard and the corresponding asset;

generating, using a graph neural network, embeddings corresponding to the entity knowledge graph and the universal knowledge graph respectively; and

performing symbolic regression, using the embeddings, to generate one or more impact functions for the target geospatial climate hazard and the corresponding asset.

2 . The computer-based method of claim 1 , further comprising:

extracting, from the first dataset, one or more of properties, dimensions, tags, and keywords.

3 . The computer-based method of claim 2 , further comprising:

filtering historical data from an accessible library based on the extracted properties, dimensions, tags, and keywords to identify the series of candidate variables related to the target geospatial climate hazard and the corresponding asset.

4 . The computer-based method of claim 1 , further comprising:

leveraging natural language processing techniques to characterize features of the natural language exchange; and

extracting, from accessible external data and publicly available data, relevant impact function formulations associated with the second dataset based on the characterized features of the natural language exchange.

5 . The computer-based method of claim 1 , wherein the predetermined set of prompts comprises multiple-choice questions.

6 . The computer-based method of claim 3 , wherein the filtered historical data from the accessible library comprises historical observation data and computational simulations.

7 . The computer-based method of claim 1 , wherein generating, using the graph neural network, the embeddings corresponding to the entity knowledge graph and the universal knowledge graph respectively further comprises:

concatenating the generated embeddings to generate a combined embedding.

8 . A computer system, the computer system comprising:

one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage medium, and program instructions stored on at least one of the one or more computer-readable tangible storage medium for execution by at least one of the one or more processors via at least one of the one or more computer-readable memories, wherein the computer system is capable of performing a method comprising:

receiving input data associated with a target geospatial climate hazard and a corresponding asset, the input data comprising one or more of a first dataset corresponding to a predetermined set of prompts, and a second dataset corresponding to a natural language exchange;

generating, based on the first dataset an entity knowledge graph including a series of candidate variables related to the target geospatial climate hazard and the corresponding asset;

generating, based on the second dataset, a universal knowledge graph including a series of candidate function formulas that are most associated with the target geospatial climate hazard and the corresponding asset;

generating, using a graph neural network, embeddings corresponding to the entity knowledge graph and the universal knowledge graph respectively; and

performing symbolic regression, using the embeddings, to generate one or more impact functions for the target geospatial climate hazard and the corresponding asset.

9 . The computer system of claim 8 , further comprising:

extracting, from the first dataset, one or more of properties, dimensions, tags, and keywords.

10 . The computer system of claim 9 , further comprising:

filtering historical data from an accessible library based on the extracted properties, dimensions, tags, and keywords to identify the series of candidate variables related to the target geospatial climate hazard and the corresponding asset.

11 . The computer system of claim 8 , further comprising:

leveraging natural language processing techniques to characterize features of the natural language exchange; and

extracting, from accessible external data and publicly available data, relevant impact function formulations associated with the second dataset based on the characterized features of the natural language exchange.

12 . The computer system of claim 8 , wherein the predetermined set of prompts comprises multiple-choice questions.

13 . The computer system of claim 10 , wherein the filtered historical data from the accessible library comprises historical observation data and computational simulations.

14 . The computer system of claim 8 , wherein generating, using the graph neural network, the embeddings corresponding to the entity knowledge graph and the universal knowledge graph respectively further comprises:

concatenating the generated embeddings to generate a combined embedding.

15 . A computer program product, the computer program product comprising:

one or more computer-readable tangible storage medium and program instructions stored on at least one of the one or more computer-readable tangible storage medium, the program instructions executable by a processor capable of performing a method, the method comprising:

receiving input data associated with a target geospatial climate hazard and a corresponding asset, the input data comprising one or more of a first dataset corresponding to a predetermined set of prompts, and a second dataset corresponding to a natural language exchange;

generating, based on the first dataset an entity knowledge graph including a series of candidate variables related to the target geospatial climate hazard and the corresponding asset;

generating, based on the second dataset, a universal knowledge graph including a series of candidate function formulas that are most associated with the target geospatial climate hazard and the corresponding asset;

generating, using a graph neural network, embeddings corresponding to the entity knowledge graph and the universal knowledge graph respectively; and

performing symbolic regression, using the embeddings, to generate one or more impact functions for the target geospatial climate hazard and the corresponding asset.

16 . The computer program product of claim 15 , further comprising:

extracting, from the first dataset, one or more of properties, dimensions, tags, and keywords.

17 . The computer program product of claim 16 , further comprising:

filtering historical data from an accessible library based on the extracted properties, dimensions, tags, and keywords to identify the series of candidate variables related to the target geospatial climate hazard and the corresponding asset.

18 . The computer program product of claim 15 , further comprising:

leveraging natural language processing techniques to characterize features of the natural language exchange; and

extracting, from accessible external data and publicly available data, relevant impact function formulations associated with the second dataset based on the characterized features of the natural language exchange.

19 . The computer program product of claim 15 , wherein the predetermined set of prompts comprises multiple-choice questions.

20 . The computer program product of claim 17 , wherein the filtered historical data from the accessible library comprises historical observation data and computational simulations.