Novel autonomous artificially intelligent system to predict pipe leaks

A computing system comprising:

• one or more data stores storing:
• a training dataset including first data items and known leaks associated with respective pipes of a first plurality of pipes, wherein the first data items include characteristics of the respective pipes;
• a validation dataset including second data items and known leaks associated with respective pipes of a second plurality of pipes, wherein the second data items include characteristics of the respective pipes;
• a computer processor;
• and
• a computer readable storage medium storing program instructions configured for execution by the computer processor in order to cause the computer processor to:
• receive a training dataset including first data items and known leaks associated with respective pipes of a first plurality of pipes, wherein the first data items include characteristics of the respective pipes, wherein the characteristics of the respective pipes include only lengths of the first plurality of pipes, soil resistivity at the first plurality of pipes, pressure rating of the first plurality of pipes, and elevation of the first plurality of pipes;
• apply a supervised machine learning technique to generate a predictive model configured to determine a leak prediction of a pipe by training the predictive model based on the first data items associated with respective pipes of the first plurality of pipes;
• receive, a validation dataset including second data items and known leaks associated with respective pipes of a second plurality of pipes, wherein the second data items include characteristics of the respective pipes;
• validate the predictive model by at least comparing a set of leak predictions of the pipes of the second plurality of pipes with known leaks of the pipes of the second plurality of pipes to determine an accuracy of the leak predictions of the pipes of the second plurality of pipes;
• access a pipeline dataset including third data items associated with a third plurality of pipes;
• and
• apply the predictive model to a pipeline dataset to determine leak predictions of respective pipes of a third plurality of pipes.

A non-transitory computer-readable medium storing a set of instructions configured for execution by a computer processor in order to cause the computer processor to:

• receive a training dataset including first data items and known leaks associated with respective pipes of a first plurality of pipes, wherein the first data items include characteristics of the respective pipes, wherein the characteristics of the respective pipes include only lengths of the first plurality of pipes, soil resistivity at the first plurality of pipes, pressure rating of the first plurality of pipes, and elevation of the first plurality of pipes;
• apply a supervised machine learning technique to generate a predictive model configured to determine a leak prediction of a pipe by training the predictive model based on the first data items associated with respective pipes of the first plurality of pipes;
• receive, a validation dataset including second data items and known leaks associated with respective pipes of a second plurality of pipes, wherein the second data items include characteristics of the respective pipes;
• validate the predictive model by at least comparing a set of leak predictions of the pipes of the second plurality of pipes with known leaks of the pipes of the second plurality of pipes to determine an accuracy of the leak predictions of the pipes of the second plurality of pipes;
• access a pipeline dataset including third data items associated with a third plurality of pipes;
• and
• apply the predictive model to a pipeline dataset to determine leak predictions of respective pipes of a third plurality of pipes.