Intelligent Online
Leakage Detection
~25%
of all drinking water in Europe is
wasted due to leakages.
The city of Hamburg records 360 pipe bursts per year.
120 billion m³
Water wasted on earth, every year
This adds up to $39 million per year, globally.
Open Data
No public datasets available.
Only case-specific data available.
Intelligent online leak detection
iOLE is at the forefront of leakage detection technology, blending two approaches — the model-based Dual Model and the data-driven LILA algorithm. This integration not only enhances efficiency but also ensures robustness, marking a new era in water loss reduction.
We aim to assist operators of drinking water networks in detecting leakages early by simplifying both the usability and evaluation of their systems. We are dedicated to ensuring the long-term effectiveness of our leakage detection tool through comprehensive training, making it accessible and user-friendly for utilities everywhere.
iOLE Solutions
The iOLE project combines two award-winning¹ leak detection algorithms recognized in scientific and academic circles. The synergies of these algorithms broaden the scope of detection and elevate the accuracy standard in leak localization.
1 ) Dual Model algorithm received the 1st place and LILA received 3rd place Award at the BattLeDIM 2020 - Battle of the Leakage Detection and Isolation Methods
DUAL MODEL
The Dual Model — originally developed by Steffelbauer, et al.², and improved by the Kompetenzzentrum Wasser Berlin — makes it possible to locate leakages down to the level of a single pipe. The algorithm automatically converts small pressure deviations caused by leaks into sharp and localized signals in the form of virtual leak flows.
2) D. B. Steffelbauer, et al., “Pressure-Leak Duality for Leak Detection and Localization in Water Distribution Systems” , J. Water Resour. Plann. Manage., vol. 148, no. 3, p. 04021106, 2022, doi: 10.1061/(ASCE)WR.1943- 5452.0001515.
LILA
TU Berlin is responsible for the development of LILA, a pressure data-driven algorithm³ that can detect leaks based on the data collected from pressure sensors throughout a water network. It detects leakages by comparing measured pressure data with regression-based estimated values at each network node⁴.
3) I. Daniel et al., “A Sequential Pressure-Based Algorithm for Data-Driven Leakage Identification and Model-Based Localization in Water Distribution Networks”, J. Water Resour. Plann. Manage., vol. 148, no. 6, p. 04022025 2022, doi: 10.1061/(ASCE)WR.1943-5452.0001535.
4) Steins, E. et al. “From theory to practice: improved algorithm for automated data-driven leakage detection in water distribution networks”. In: Journal of Water Resources Planning and Management.
By combining detection result with both algorithms connected with a common anomaly detection step — adaptive CUSUM, iOLE offers sensitive analysis and algorithm robustness against multiple factor variations.
Questions about iOLE tech? Want to learn more about our research?
Talk to us about iOLE’s technology and real-world capabilities. Explore collaboration opportunities
See iOLE in action
Experience how our leak detection algorithms translate pressure data into accurate leak localization.
Partner with iOLE
Collaborate on innovative water network solutions, co-develop technology or explore sale or strategic opportunities.
Project Goals
To ensure widespread implementation of our technology we put great emphasis on accessibility so that Operators and Maintenance personnel adopt the tool in their workflow.
Our tool prioritizes comprehensive and intuitive visualization using GIS data integration to increase accessibility and intelligently assist responsible personnel in decision-making.
By fusing two cutting-edge algorithms, we are able to address a wider spectrum of water leakage detection needs. This integration not only ensures a higher level of automation but also guarantees robustness by bringing in different approaches under the same software envelope.
Integrated Models
Combined Robustness & Automation
Human-Centered
Tech Design
PROJECT UPDATES
PROJECT UPDATES
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Publications & Press
KWB Annual Report
The iOLE project is presented as one of the projects in the 2024 annual report of the Kompetenzzentrum Wasser Berlin
(from p. 30).
Project Partners
Funded by
iOLE is a Digital GreenTech project funded by the German Federal Ministry of Education and Research.
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We are committed to advancing iOLE solutions and bringing them into real-world operations.
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Get in touch with us to explore collaboration opportunities.
Andrea Cominola
Professor and Head of the Digital Water Systems Lab | Einstein Center Digital Future and Technische Universität Berlin