Hail predictions using artificial intelligence
The disclosure includes systems and methods for receiving a location; determine a hail size associated with the location using a first hail model; determine a hail frequency associated with the location using a first hail frequency model; obtain first feature data associated with the location, the first feature data including the hail size associated with the location, the hail frequency associated with the location, and data describing a first set of features at the location; determine a damage frequency associated with the location by applying a first damage frequency model to the first feature data; obtain second feature data associated with the location, the second feature data including data describing a second set of features at the location; and determine a damage severity associated with the location by applying a first damage severity model to the second feature data.
1. A computer implemented method comprising:
receiving, using one or more processors, a location;
training, using the one or more processors, a first hail machine learning model that discounts older hail data;
determine, using the one or more processors, a hail size associated with the location using the first hail machine learning model;
determine, using the one or more processors, a hail frequency associated with the location using a first hail frequency machine learning model;
obtain, using the one or more processors, first feature data associated with the location, the first feature data including the hail size associated with the location, the hail frequency associated with the location, and data describing a first set of features at the location;
determine, using the one or more processors, a damage frequency associated with the location by applying a first damage frequency machine learning model to the first feature data;
obtain, using the one or more processors, second feature data associated with the location, the second feature data including data describing a second set of features at the location; and
determine, using the one or more processors, a damage severity associated with the location by applying a first damage severity machine learning model to the second feature data.
2. The computer implemented method of claim 1 , wherein the location is represented by a latitude and longitude.
3. The computer implemented method of claim 1 , wherein the hail size represents one or more of an average hail size associated with the location and a maximum hail size associated with the location.
4. The computer implemented method of claim 1 , wherein hail frequency represents one or more of a probability of a hail event and a probability of a hail event in which hail exceeds a size threshold.
5. The computer implemented method of claim 1 , wherein one or more of the first set of features and the second set of features include one or more of: a building area, a vegetation density, a roof material, a roof quality, a roof pitch, a roof height, presence of skylights, a portion of a roof covered by skylights, presence of a solar panel, a portion of the roof covered by solar panels, a number of roof facets, a roof shape, a land cover code, a temperature, a precipitation type or measure, and an elevation.
6. The computer implemented method of claim 1 , wherein the first set of features at the location includes a first feature that is obtained actively by applying a feature model to an aerial image of the location.
7. The computer implemented method of claim 6 , wherein the feature model is a convolutional neural network.
8. The computer implemented method of claim 1 further comprising:
determining, based on one or more of the damage frequency and damage severity, one or more of: a remedial action to reduce a risk of hail; whether to approve or deny hail insurance coverage or an insurance claim; an insurance premium associated with the location, an adjustment to an insurance premium associated with location; and to warn one or more of a property owner, resident, financier and insurer associated with the location of a risk of damage posed by hail.
9. The computer implemented method of claim 1 , wherein the first set of features at the location and the second set of features at the location are not mutually exclusive.
10. The computer implemented method of claim 1 , wherein the second set of features includes one or more of a roof area, a building area, a vegetation density, a roof material, a roof quality, a roof pitch, a roof height, presence of skylights, a portion of a roof covered by skylights, presence of a solar panel, a portion of the roof covered by solar panels, a number of roof facets, a roof shape, a land cover code, a temperature, a precipitation type or metric, and an elevation.
11. A system comprising:
a processor; and
a memory, the memory storing instructions that, when executed by the processor, cause the system to:
receive a location;
train a first hail machine learning model that discounts older hail data;
determine a hail size associated with the location using the first hail machine learning model;
determine a hail frequency associated with the location using a first hail frequency machine learning model;
obtain first feature data associated with the location, the first feature data including the hail size associated with the location, the hail frequency associated with the location, and data describing a first set of features at the location;
determine a damage frequency associated with the location by applying a first damage frequency machine learning model to the first feature data;
obtain second feature data associated with the location, the second feature data including data describing a second set of features at the location; and
determine a damage severity associated with the location by applying a first damage severity machine learning model to the second feature data.
12. The system of claim 11 , wherein the location is represented by a latitude and longitude.
13. The system of claim 11 , wherein the hail size represents one or more of an average hail size associated with the location and a maximum hail size associated with the location.
14. The system of claim 11 , wherein hail frequency represents one or more of a probability of a hail event and a probability of a hail event in which hail exceeds a size threshold.
15. The system of claim 11 , wherein one or more of the first set of features and the second set of features include one or more of: a building area, a vegetation density, a roof material, a roof quality, a roof pitch, a roof height, presence of skylights, a portion of a roof covered by skylights, presence of a solar panel, a portion of the roof covered by solar panels, a number of roof facets, a roof shape, a land cover code, a temperature, a precipitation type or metric, and an elevation.
16. The system of claim 11 , wherein the first set of features at the location includes a first feature that is obtained actively by applying a feature model to an aerial image of the location.
17. The system of claim 16 , wherein the feature model is a convolutional neural network.
18. The system of claim 11 , the memory further storing instructions that, when executed by the processor, cause the system to:
determine, based on one or more of the damage frequency and damage severity, one or more of: a remedial action to reduce a risk of hail; whether to approve or deny hail insurance coverage or an insurance claim; an insurance premium associated with the location, an adjustment to an insurance premium associated with location; and to warn one or more of a property owner, resident, financier and insurer associated with the location of a risk of damage posed by hail.
19. The system of claim 11 , wherein the first set of features at the location and the second set of features at the location are not mutually exclusive.
20. The system of claim 11 , wherein the second set of features includes one or more of a roof area, a building area, a vegetation density, a roof material, a roof quality, a roof pitch, a roof height, presence of skylights, a portion of a roof covered by skylights, presence of a solar panel, a portion of the roof covered by solar panels, a number of roof facets, a roof shape, a land cover code, a temperature, a precipitation type or metric, and an elevation.