Wind Predictions Using Artificial Intelligence
The disclosure includes systems and methods for receiving, using one or more processors, a location, determining a wind speed associated with the location using a first wind speed machine learning model, determining a wind report frequency associated with the location using a first wind report frequency machine learning model, obtaining first feature data associated with the location including the wind speed associated with the location, the wind report frequency associated with the location, and data describing a first set of features at the location, determining a damage frequency metric associated with the location by applying a first damage frequency machine learning model to the first feature data, obtaining second feature data associated with the location including data describing a second set of features at the location, and determining a damage severity metric associated with the location by applying a first damage severity machine learning model to the second feature data.
1 . A computer implemented method comprising:
receiving, using one or more processors, a location;
determining, using the one or more processors, a wind speed associated with the location using a first wind speed machine learning model;
determining, using the one or more processors, a wind report frequency associated with the location using a first wind report frequency machine learning model;
obtaining, using the one or more processors, first feature data associated with the location, the first feature data including the wind speed associated with the location, the wind report frequency associated with the location, and data describing a first set of features at the location;
determining, using the one or more processors, a damage frequency metric associated with the location by applying a first damage frequency machine learning model to the first feature data;
obtaining, 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
determining, using the one or more processors, a damage severity metric 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 wind speed represents an average wind speed associated with the location.
4 . The computer implemented method of claim 1 , wherein wind report frequency represents one or more of a frequency of a wind report and a frequency of a wind report having an average wind speed that exceeds a threshold.
5 . The computer implemented method of claim 1 , wherein one or more of the first set of features includes one or more of: a vegetation density, a roof resilience score, a number of roof penetrations, a roof quality, one or more reasons generated by a roof quality reasoning model, a land cover code, a temperature, and a precipitation metric.
6 . The computer implemented method of claim 1 , wherein the one or more features at the location include a first feature obtained 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 metric and the damage severity metric, one or more of: a remedial action to reduce a wind damage metric; a determination of the wind damage metric; a warning to one or more of a property owner, a resident, and an entity associated with the location, the warning comprising the wind damage metric.
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 building area, a vegetation density, a roof material, a roof quality, a roof pitch, a roof height, a roof shape, a temperature, a temperature variation, and a precipitation metric.
11 . A system comprising:
one or more processors; and
a memory, the memory storing instructions that, when executed by the one or more processors, cause the system to:
receive, using the one or more processors, a location;
determine, using the one or more processors, a wind speed associated with the location using a first wind speed machine learning model;
determine, using the one or more processors, a wind report frequency associated with the location using a first wind report frequency machine learning model;
obtain, using the one or more processors, first feature data associated with the location, the first feature data including the wind speed associated with the location, the wind report 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 metric 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 metric 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 wind speed represents an average wind speed associated with the location.
14 . The system of claim 11 , wherein wind report frequency represents one or more of a frequency of a wind report and a frequency of a wind report having an average wind speed that exceeds a threshold.
15 . The system of claim 11 , wherein one or more of the first set of features includes one or more of: a vegetation density, a roof resilience score, a number of roof penetrations, a roof quality, one or more reasons generated by a roof quality reasoning model, a land cover code, a temperature, and a precipitation metric.
16 . The system of claim 11 , wherein the one or more features at the location include a first feature obtained 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 , wherein the instructions further cause the system to:
determine, based on one or more of the damage frequency metric and the damage severity metric, one or more of: a remedial action to reduce a wind damage metric; a determination of the wind damage metric; a warning to one or more of a property owner, a resident, and an entity associated with the location, the warning comprising the wind damage metric.
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 building area, a vegetation density, a roof material, a roof quality, a roof pitch, a roof height, a roof shape, a temperature, a temperature variation, and a precipitation metric.