Weather-driven multi-category infrastructure impact forecasting

1. A non-transitory computer program product, the non-transitory computer program product comprising a computer readable storage medium having instructions embodied therewith, the instructions executable by at least one processor to perform a method comprising:

selecting a plurality of trouble regions within a service area;

generating clustered regions, each of the clustered regions including at least one of the trouble regions within the service area and each of the trouble regions within the service area being associated with one of the clustered regions, the at least one of the trouble region of each cluster region being generated based on a distance between the at least one of the trouble region and other trouble regions of the plurality of trouble regions within the service area;

for each of the trouble regions:

training a plurality of trouble forecast models for each type of weather-related damage, each of the plurality of trouble forecast model being for a different type of weather-related damage, the training for each of the plurality of trouble forecast models including:

receiving historical weather data from a plurality of weather data sources, the historical weather data being from at least a portion of the service area including a particular trouble region;

filtering out low confidence historical weather data with data quality filters, each of the quality filters being specific to one of the plurality of weather data sources; and

normalizing the historical weather data for a particular weather-related damage to a common repeating time interval to obtain a normalized time series for a particular weather feature, the normalized being based on one of a minimum, maximum, an average, or a rate of change; and

applying each of the trouble forecast models for the at least one clustered region within the service area to obtain a trouble forecast for the particular trouble region within the service area for each type of the weather-related damage;

determining a regional forecast for a geographic service area based on the trouble forecast for the geographic service area in order to facilitate management of resources by relocating equipment and personnel within the service area; and

providing the regional forecast for the geographic service area based on the trouble forecast, in order to minimize an impact of the trouble forecast.

2. The computer program product of claim 1 , wherein the impact comprises an amount of time required to resolve weather-related damage to the resources.

3. The computer program product of claim 1 , wherein the impact comprises an amount of time required to resolve a weather-related interruption to service in the service area.

4. The computer program product of claim 1 , wherein the training the plurality of trouble forecast models is further based on trouble history records.

5. The computer program product of claim 1 further comprising collecting historical data from one of a National Oceanic and Atmospheric Administration (NOAA) Metar station or a NOAA Mesonet station.

6. The computer program product of claim 1 , wherein generating clustered regions further comprises generating cluster regions based on similarity of the trouble regions in at least one of job type or weather-related damage.

7. The computer program product of claim 1 , wherein determining the job forecast for the service area based on the service area comprises determining a forecasting horizon for each trouble region and each type of weather-related damage.

8. The computer program product of claim 7 further comprises, dividing the forecasting horizon into a plurality of prediction periods.

9. The computer program of claim 8 , wherein determining the job forecast for the service area based on the service area further comprises computing, for each trouble region, trouble type and the plurality of prediction periods, a trouble count forecast based on a probability distribution.

10. A system for managing resources based on weather-related damage in a service area, the system comprising:

at least one processor; and

memory including instructions executable by the at least one processor to configure the at least one processor to:

selecting a plurality of trouble regions within a service area;

generating clustered regions, each of the clustered regions including at least one of the trouble regions within the service area and each of the trouble regions within the service area being associated with one of the clustered regions, the at least one of the trouble region of each cluster region being generated based on a distance between the at least one of the trouble region and other trouble regions of the plurality of trouble regions within the service area;

for each of the trouble regions:

training a plurality of trouble forecast models for each type of weather-related damage, each of the plurality of trouble forecast model being for a different type of weather-related damage, the training for each of the plurality of trouble forecast models including:

receiving historical weather data from a plurality of weather data sources, the historical weather data being from at least a portion of the service area including a particular trouble region;

filtering out low confidence historical weather data with data quality filters, each of the quality filters being specific to one of the plurality of weather data sources; and

normalizing the historical weather data for a particular weather-related damage to a common repeating time interval to obtain a normalized time series for a particular weather feature, the normalized being based on one of a minimum, maximum, an average, or a rate of change; and

applying each of the trouble forecast models for the at least one clustered region within the service area to obtain a trouble forecast for the particular trouble region within the service area for each type of the weather-related damage;

determining a regional forecast for a geographic service area based on the trouble forecast for the geographic service area in order to facilitate management of resources by relocating equipment and personnel within the service area; and

providing the regional forecast for the geographic service area based on the trouble forecast, in order to minimize an impact of the trouble forecast.

11. The system of claim 10 , wherein the impact comprises an amount of time required to resolve weather-related damage to the resources.

12. The system of claim 10 , wherein the impact comprises an amount of time required to resolve a weather-related interruption to service in the service area.

13. The system of claim 10 , wherein the training the plurality of trouble forecast models is further based on trouble history records.

14. The system of claim 10 , further comprising collecting historical weather data from one of a National Oceanic and Atmospheric Administration (NOAA) Metar station or a NOAA Mesonet station.

15. The system of claim 10 , wherein generating clustered regions further comprises generating cluster regions based on similarity of the trouble regions in at least one of job type or weather-related damage.

16. The system of claim 10 , wherein determining the job forecast for the service area based on the service area comprises determining a forecasting horizon for each trouble region and each type of weather-related damage.

17. The system of claim 16 , wherein the at least one processor is configured to determine the job forecast for the service area based on the service area further comprises the at least one processor configured to compute, for each trouble region and type of weather-related damage, a trouble count forecast based on a probability distribution.

18. The system of claim 17 , wherein determine the job forecast for the service area based on the service area further comprises computing, for each trouble region, trouble type and the plurality of prediction periods, a trouble count forecast based on a probability distribution.