Case study
Agentschap Telecom

AI points inspectors to the greatest risk

How Agentschap Telecom uses data and machine learning to predict the likelihood of cable strike damage — helping inspectors focus their attention on the situations where the risk is greatest.

OCI-infrastructuur met een Oracle-omgeving en Microsoft Azure-omgeving erboven
Customer Agentschap Telecom
Context Supervising compliance with the WIBON
Expertise Data Platforms, Analytics & Intelligence, AI & Applications
Solution Predictive risk model for cable strike damage

More targeted supervision of hundreds of thousands of excavation works

Around 700,000 mechanical excavation works take place in the Netherlands every year. Agentschap Telecom supervised compliance with the WIBON and carried out hundreds of inspections annually.

Damage to underground cables and pipes can have major economic and social consequences — direct repair costs rose to more than €38 million in 2020. At the same time, it's impossible to inspect every excavation activity: the challenge wasn't to carry out more inspections, but to better determine where supervision could have the greatest impact.

Our approach

Combining data to make risk predictable

  1. Gathering data

    Map data, planned fibre-optic works, excavation notifications, weather information and historical damage data brought together in a single application.

  2. Modelling risk

    Full Orbit worked with Agentschap Telecom, DICTU and Oracle to translate this data into a predictive machine learning model.

  3. Predicting the likelihood of damage

    A Random Forest model estimates the likelihood of damage for each excavation notification, based on historical and current characteristics.

  4. Prioritising inspections

    Inspectors can focus their limited capacity more effectively on the notifications with the highest risk.

A risk score for every excavation notification

In an interactive application, inspectors could see relevant information about excavation notifications in their area in one place — from current map data to planned fibre-optic works and historical damage data.

On top of that, a machine learning model (Random Forest) predicts, based on historical data, how likely cable strike damage is for a new excavation notification. The result: every notification gets a risk indicator that helps inspectors prioritise their work more effectively.

The core

Data drives the inspection schedule

By combining multiple data sources and developing a predictive model, Agentschap Telecom gained a data-driven basis for deploying scarce inspection capacity more effectively.

Download the case study
Multiple data sources combined Map data, fibre-optic works, excavation notifications, weather and historical damage in a single application.
Predictive risk model A Random Forest model estimates the likelihood of cable strike damage for each excavation notification.
Risk-based supervision Inspectors focus their attention on the notifications with the highest risk.

A concrete foundation for risk-based supervision

82%

Accurate predictive model

The predictive model classified 82% of predictions correctly — evidence that historical and current data can reliably estimate the likelihood of cable strike damage.

6 → 2 hrs

Faster model training

By running machine learning closer to the data (Oracle Machine Learning for R), training time for the model was significantly reduced.

62%

Reliable risk prediction

62% of the damage cases predicted by the model turned out to be genuine damage (PPV) — an important indicator for risk-based supervision, where inspectors focus their attention on the notifications with the highest predicted likelihood.

The high economic damage caused by cable strikes, combined with having access to a large number of relevant data sources, made fibre-optic-related cable strike damage a relevant theme for us to start a pilot. The aim of this was to use data analysis to reduce the number of cable strike damage reports.

Dr. Ryanne van Dalen Data Scientist, Agentschap Telecom

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