SYSTEMS AND METHODS FOR PIPELINE RISK MODELING

1 . A method for reducing pipe failure risk, comprising:

receiving, by a processor, an image depicting an overhead view of an area and a set of pipe data indicating characteristics for an underground pipe that is located within the area;

receiving, by the processor, a set of geospatial data for a geographic region in which the area is located;

segmenting, by the processor, the set of pipe data and the set of geospatial data for the geographic region into a plurality of segments to generate a feature vector, each of the plurality of segments corresponding to a separate portion of the underground pipe;

executing, by the processor, a machine learning model using the feature vector to generate failure likelihood data for the separate portions of the underground pipe;

determining, by the processor, visual indicators that correspond to the generated failure likelihood data for the separate portions of the underground pipe; and

generating, by the processor, an overlay from the visual indicators, the overlay comprising the visual indicators for pixels of the image that correspond to the separate portions of the underground pipe.

2 . The method of claim 1 , wherein segmenting the set of pipe data and the set of geospatial data comprises discarding, by the processor, geospatial data from the set of geospatial data responsive to the geospatial data corresponding to a location above a predetermined distance from the underground pipe.

3 . The method of claim 1 , further comprising training, by the processor, the machine learning model using historical failure data of the separate portions of the underground pipe.

4 . The method of claim 1 , further comprising training, by the processor, the machine learning model by:

receiving, by the processor, a second set of pipe data and failure data for the separate portions of the underground pipe from a first time period, the failure data indicating whether a failure occurred in each of the respective portions of the underground pipe during the first time period;

receiving, by the processor, a second set of geospatial data for the geographic region in which the area is located from the first time period;

segmenting, by the processor, the second set of pipe data and the second set of geospatial data for the geographic region into a second plurality of segments to generate a second feature vector, each of the second plurality of segments corresponding to a separate portion of the underground pipe;

using the failure data, labeling, by the processor, each of the second plurality of segments of the feature vector with a flag indicating whether a failure occurred in the portion of the underground pipe that corresponds to the respective segment during the first time period; and

training, by the processor, the machine learning model with the feature vector comprising the labeled segments.

5 . The method of claim 4 , wherein training the machine learning model comprises:

receiving, by the processor, a third set of pipe data and second failure data for the separate portions of the underground pipe from a second time period subsequent to the first time period, the second failure data indicating whether a failure occurred in each of the respective separate portions of the underground pipe during the second time period;

receiving, by the processor, a third set of geospatial data for the geographic region in which the area is located from the second time period;

segmenting, by the processor, the third set of pipe data and the third set of geospatial data for the geographic region into a third plurality of segments to generate a third feature vector, each of the third plurality of segments corresponding to a separate portion of the underground pipe;

executing, by the processor, the machine learning model to generate second failure likelihood data for the separate portions of the underground pipe; and

comparing, by the processor, the second failure likelihood data with the second failure data to determine an accuracy of the machine learning model.

6 . The method of claim 5 , further comprising:

provisioning, by the processor, the machine learning model in response to the accuracy of the machine learning model exceeding a threshold.

7 . The method of claim 1 , wherein receiving the set of geospatial data for a geographic region in which the area is located comprises receiving, by the processor, terrain motion timeseries data, vegetation presence data, soil property data, or terrain slope data.

8 . The method of claim 1 , wherein receiving the set of pipe data comprises receiving, by the processor, pipe diameter data, pipe material data, or pipe age data.

9 . The method of claim 1 , wherein determining the visual indicators comprises selecting, by the processor, a color for each portion of the underground pipe based on the failure likelihood data for the respective portions of the underground pipe.

10 . The method of claim 1 , wherein segmenting the set of pipe data and the set of geospatial data for the geographic region comprises:

determining, by the processor, whether a portion of the underground pipe has been labeled with an active or replaced label in the set of pipe data; and

discarding, by the processor, pipe data and geospatial data for the portion of the underground pipe in response to determining the portion of the underground pipe has not been labeled with an active or replaced label.

11 . The method of claim 1 , wherein segmenting the set of pipe data and the set of geospatial data for the geographic region comprises:

determining, by the processor, whether the set of pipe data comprises a material value, a diameter value, and an age value for a portion of the underground pipe; and

discarding, by the processor, pipe data and geospatial data for the portion of the underground pipe in response to determining the set of pipe data does not comprise one of a material value, a diameter value, or an age value for the underground pipe.

12 . The method of claim 1 , wherein the failure likelihood data for a portion of the underground pipe comprises a likelihood that a failure will occur in the portion of the underground pipe, and wherein determining the visual indicator that corresponds to the generated failure likelihood data for the separate portions of the underground pipe comprises:

identifying, by the processor, a sub-region of the area that contains failure likelihood data for portions of the underground pipe with an average above a threshold; and

selecting, by the processor, a color for each portion of the underground pipe that is located within the sub-region of the area based on the average being above the threshold.

13 . The method of claim 1 , wherein determining the visual indicator that corresponds to the generated failure likelihood data for the separate portions of the underground pipe comprises:

selecting, by the processor, a color for a portion of the underground pipe based on a consequence severity if the portion of the underground pipe experiences a failure.

14 . The method of claim 1 , wherein determining the visual indicator that corresponds to the generated failure likelihood data for the separate portions of the underground pipe comprises:

determining, by the processor, a criticality score for a portion of the underground pipe based on a consequence severity of the underground pipe if the portion of the underground pipe experiences a failure and the failure likelihood data for the portion of the underground pipe; and

selecting, by the processor, a color for the portion of underground pipe based on the criticality score.

15 . The method of claim 14 , wherein determining the criticality score comprises determining, by the processor, a weighted average of the consequence severity and the failure likelihood data for the portion of the underground pipe.

16 . A system for reducing pipe failure risk, comprising:

a processor configured by machine-readable instructions to:

receive an image depicting an overhead view of an area and a set of pipe data indicating characteristics for an underground pipe that is located within the area;

receive a set of geospatial data for a geographic region in which the area is located;

segment the set of pipe data and the set of geospatial data for the geographic region into a plurality of segments to generate a feature vector, each of the plurality of segments corresponding to a separate portion of the underground pipe;

execute a machine learning model using the feature vector to generate failure likelihood data for the separate portions of the underground pipe;

determine visual indicators that correspond to the generated failure likelihood data for the separate portions of the underground pipe; and

generate an overlay from the visual indicators, the overlay comprising the visual indicators for pixels of the image that correspond to the separate portions of the underground pipe.

17 . The system of claim 16 , wherein the processor is configured to segment the set of pipe data and the set of geospatial data by discarding geospatial data from the set of geospatial data responsive to the geospatial data corresponding to a location above a predetermined distance from the underground pipe.

18 . The system of claim 16 , wherein the processor is further configured to train the machine learning model using historical failure data of the separate portions of the underground pipe.

19 . A non-transitory computer-readable storage medium having instructions embodied thereon, the instructions being executable by a processor to perform a method for reducing pipe failure risk, the method comprising:

receiving an image depicting an overhead view of an area and a set of pipe data indicating characteristics for an underground pipe that is located within the area;

receiving a set of geospatial data for a geographic region in which the area is located;

segmenting the set of pipe data and the set of geospatial data for the geographic region into a plurality of segments to generate a feature vector, each of the plurality of segments corresponding to a separate portion of the underground pipe;

executing a machine learning model using the feature vector to generate failure likelihood data for the separate portions of the underground pipe;

determining visual indicators that correspond to the generated failure likelihood data for the separate portions of the underground pipe; and

generating an overlay from the visual indicators, the overlay comprising the visual indicators for pixels of the image that correspond to the separate portions of the underground pipe.

20 . The non-transitory computer-readable storage medium of claim 19 , wherein segmenting the set of pipe data and the set of geospatial data comprises discarding geospatial data from the set of geospatial data responsive to the geospatial data corresponding to a location above a predetermined distance from the underground pipe.