Processing data for predicting pipe failure

A system for improving pipe networks of underground pipes for carrying fluid to consumers, the system comprising:

• a database that stores a set of pipe data that includes a plurality of pipe attributes for one or more of said networks of underground pipes;
• a processing system configured to identify one or more pipes in at least on of said networks for which one or more pipe attributes is incorrect, generate imputed data for the two or more pipe attributes for pipe material, diameter, installation year, and surface area resulting in an improved set of pipe data, wherein one or more of aspects of one or more of said networks of underground pipes can be managed based at least in part on the improved set of pipe data;
• wherein the processing system is further configured to predict likelihood of pipe segments in the said networks leaking, including of an individual pipe that has never leaked;
• and
• said one or more of aspects of one or more of said networks of underground pipes being managed includes replacing pipe sections based at least in part on said predicted likelihood of leaking.

A method of improving networks of underground pipes, the method comprising:

• accessing a first plurality of separate environmental raster form datasets;
• merging at least some of the first plurality of separate environmental raster form datasets to generate a second plurality of larger environmental form datasets;
• vectorizing at least some of second plurality of larger environmental form datasets to generate a third plurality of larger vector datasets;
• merging at least some of the third plurality of larger vector datasets to form a merged vector dataset and imputing attributes of individual pipe segments that are incorrect in or missing from the separate environmental raster form databases, wherein the imputed attributes include pipe material and pipe diameter;
• and
• replacing pipe sections based in part on said imputed missing attributes in the merged vector dataset;
• wherein said replacing comprises selecting pipe sections for replacement based at least on predicting likelihood of failure of individual pipe segments, including of an individual pipe segment that has never leaked, and building a model using machine learning based at least in part on said standardized database, and wherein said predicting likelihood of individual pipe segments leaking is based on the model.