Michael Gloven, Managing Partner, EIS
This session outlines how to use RapidMiner to support investment & maintenance decision-making for linear and networked assets such as pipelines, roads, electric transmission lines, water distribution systems, etc. Join as Michael demonstrates how machine learning can predict undesirable events and monetized risk for linear and networked assets.
These results may then be used to support specific risk mitigation strategies and budget plans. The objective is to put in place a more strategic data-driven approach to resource decision-making, which should improve the risk profile and profitability of the asset owner. Key to the presentation is demonstrating important considerations for the application of machine learning to these types of assets.