Automatic weather event impact estimation
A processor may receive weather event data. The processor may determine, utilizing an artificial intelligence model mapping weather events having weather impacts with a higher likelihood of occurrence proximate to each other in the latent space, a weather impact associated with a weather event. In some embodiments, the artificial intelligence model may be trained using historical weather event data and historical weather impact data associated with the historical weather event data. The processor may output the weather impact associated with the weather event to a user.
1. A computer-implemented method, the method comprising:
receiving, by a processor, data corresponding to a future weather event, wherein a portion of the data is collected using a sensor;
identifying an artificial intelligence (“AI”) model, wherein the AI model has been trained using historical weather event data, historical weather impact data associated with the historical weather event data, and contextual information, wherein the contextual information includes data about the sensor;
mapping weather events having weather impacts with a higher likelihood of occurrence proximate to each other in a latent space in the context of the artificial intelligence model;
determining, utilizing the AI model, a weather impact associated with the future weather event; and
outputting the weather impact associated with the future weather event to a user, wherein the output weather impact comprises an estimate of property damage monetary cost, a prediction of types of property damage resulting from the future weather event, and a repair time;
wherein determining the weather impact associated with the future weather event includes interpolating weather impacts of neighboring weather events in the latent space.
2. The computer-implemented method of claim 1 , further comprising:
outputting an estimated likelihood of the weather impact.
3. The computer-implemented method of claim 1 , further comprising:
outputting an estimated likelihood of occurrence of the future weather event.
4. The computer-implemented method of claim 1 , the method further comprising:
selecting a portion of contextual information associated with generating the AI model.
5. The computer-implemented method of claim 1 , wherein the AI model utilizes a variational auto-encoder neural network.
6. The computer-implemented method of claim 1 , wherein the AI model maps historical weather event data into a probabilistic distribution.
7. A system comprising:
a memory; and
a processor in communication with the memory, the processor being configured to perform operations comprising:
receiving, by a processor, data corresponding to a future weather event, wherein a portion of the data is collected using a sensor;
identifying an artificial intelligence (“AI”) model, wherein the AI model has been trained using historical weather event data, historical weather impact data associated with the historical weather event data, and contextual information, wherein the contextual information includes data about the sensor;
mapping weather events having weather impacts with a higher likelihood of occurrence proximate to each other in a latent space in the context of the artificial intelligence model;
determining, utilizing the AI model, a weather impact associated with the future weather event; and
outputting the weather impact associated with the future weather event to a user, wherein the output weather impact comprises an estimate of property damage monetary cost, a prediction of types of property damage resulting from the future weather event, and a repair time;
wherein determining the weather impact associated with the weather event includes interpolating weather impacts of neighboring weather events in the latent space.
8. The system of claim 7 , the processor being configured to perform operations comprising:
outputting an estimated likelihood of the weather impact.
9. The system of claim 7 , the processor being configured to perform operations comprising:
outputting an estimated likelihood of occurrence of the future weather event.
10. The system of claim 7 , wherein the processor is further configured to perform operations comprising:
selecting a portion of contextual information associated with generating the AI model.
11. The system of claim 7 , wherein the AI model utilizes a variational auto-encoder neural network.
12. The system of claim 7 , wherein the AI model maps historical weather event data into a probabilistic distribution.
13. A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to perform operations, the operations comprising:
receiving, by a processor, data corresponding to a future weather event, wherein a portion of the data is collected using a sensor;
identifying an artificial intelligence (“AI”) model, wherein the AI model has been trained using historical weather event data, historical weather impact data associated with the historical weather event data, and contextual information, wherein the contextual information includes data about the sensor;
mapping weather events having weather impacts with a higher likelihood of occurrence proximate to each other in a latent space in the context of the artificial intelligence model;
determining, utilizing the AI model, a weather impact associated with the future weather event; and
outputting the weather impact associated with the future weather event to a user, wherein the output weather impact comprises an estimate of property damage monetary cost, a prediction of types of property damage resulting from the future weather event, and a repair time;
wherein determining the weather impact associated with the weather event includes interpolating weather impacts of neighboring weather events in the latent space.
14. The computer program product of claim 13 , the processor being configured to perform operations comprising:
outputting an estimated likelihood of the weather impact.
15. The computer program product of claim 13 , the processor being configured to perform operations comprising:
outputting an estimated likelihood of occurrence of the future weather event.
16. The computer program product of claim 13 , the processor being configured to perform operations comprising:
selecting a portion of contextual information associated with generating the AI model.
17. The computer program product of claim 13 , wherein the AI model maps historical weather event data into a probabilistic distribution.