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leadCAST Predict

Gain control over unknowns in your service line inventory using machine learning.
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How it works

leadCAST Predict helps you accelerate the classification of unknown service lines and prioritize field verifications through advanced service line material predictions powered by machine learning. leadCAST Predict is an additional feature of leadCAST LCRR compliance management solution. leadCAST Predict uses three main elements.

Data

Combine your service line inventory with parcel and census data to predict unknown materials. Our machine-learning experts use statistical techniques to prepare, clean, and improve your data to support optimal modeling.

Model

There is no one-size-fits-all modeling approach for predicting service line material. Our machine learning experts evaluate the performance and reliability of many different types of machine learning models to select the best fit for your data.

Results

Service line material predictions are viewable in an interactive dashboard. A final report describes how the machine learning model was built and tested, so that non-technical stakeholders can understand how the model works and have confidence in the model's predictions.

How much will your utility save?

Meet our data scientist team

Katie Deheer MS, MBA

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she/her
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Katie is the Senior Data Analyst for Trinnex and architect of leadCAST Predict, the predictive modeling module within leadCAST. Katie has over 12 years of experience in analytics-driven research, machine learning, and data visualization. Katie has led the development of predictive pipe material models for clients such as Salem, VA.

Mark Zito, GISP, CFM

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he/him
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Mark is the senior product manager and architect of leadCAST and has over 15 years of experience helping utilities implement software solutions. Mark has executed over a dozen lead and copper-related projects including the award-winning Newark Lead Line Replacement Program, where he designed a data-driven solution to track the lead mitigation lifecycle.

Shervin Khazaeli, Ph.D.

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he/him
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Shervin is a data analytics developer and the lead data scientist for leadCAST Predict with a Ph.D. in artificial intelligence (AI) focusing on probabilistic decision-making. Shervin uses over 50 parameters in the leadCAST Predict model to optimize accuracy. He applies data science and statistical techniques to prepare, clean, and improve existing data to support optimal modeling.

Get in touch with our data scientist team.
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Why use leadCAST Predict?

The reality is that water utilities just don’t have the financial and staffing resources to verify every single service line. The challenges such as preparing for impending cybersecurity standards or finding staff, add to the pressure most utilities face every day. It can take years to complete field verifications and our communities deserve better than that. leadCAST Predict exists for one purpose only: to help you identify lead more quickly and accurately, so that you can remove and replace lines expeditiously.
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What you'll get with leadCAST Predict

3 people huddled around a computer, the women standing is pointing to something on the computer screen.

Domain expertise quality checks

Our data scientists implement the leadCAST Predict machine learning model with the best available property and service line data, and identify which data elements or features are most influential in predicting service line material. Our data scientists work with subject matter experts to understand and explain dominant model features, and that can optimize model performance. Using quality data and a trained model, we can successfully predict service line material with accuracy comparable to physical verification methods.

An easy-to-follow predictions report

Following model configuration, our data science team prepares a report narrating how the machine learning model for your particular system was built and tested. The report provides performance metrics for understanding the accuracy and reliability of the model.
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a screenshot of the leadCAST dashboard showing the zoomed in map view of a neighborhood
Detailed view
a screenshot of the leadCAST dashboard showing the default view of a neighborhood
a screenshot of the leadCAST dashboard showing the satellite map view of a neighborhood
Street view
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Dynamic Map Views

leadCAST enables the user to choose from three different map views showing machine learning prediction results. These views include a zoomed-out view for a high-level view of where potential lead might exist, a detailed view showing specific neighborhoods or streets, and a street view. You’ll also have access to an exportable report that summarizes the results.

Inspection optimizer

Evaluate whether existing field verifications are truly representative of your entire water system or if additional verifications are needed. Gain a better understanding of which properties to inspect to obtain a representative sample for unbiased and reliable predictive modeling.
Recommended inspections to obtain representative sample.

Learn more

The Trinnex team can walk you through how leadCAST Predict works and how it can be configured for your utility. Reach out if you have any questions or would like a full demo of leadCAST with leadCAST Predict.
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