Papers by Marios Kyriakou
The increase of streaming data from water utilities is enabling the development of real-time anom... more The increase of streaming data from water utilities is enabling the development of real-time anomaly and fault detection algorithms that can detect events, such as pipe bursts and leakages. Currently, there is not a widely accessible dataset of real or realistic leakage scenarios, which could be used as a common benchmark to compare different algorithms, as well as to support research reproducibility. In this work we propose the design of a realistic leakage dataset, the Leakage Diagnosis Benchmark (LeakDB). The dataset is comprised of a large number of artificially created but realistic leakage scenarios, on different water distribution networks, under varying conditions. Additionally, a scoring algorithm was developed in MATLAB to evaluate the results of different algorithms using various metrics. The usage of the LeakDB dataset, is demonstrated by scoring four detection algorithms. The dataset is stored on an open research data repository, and will be updated in the future with new simulation scenarios. The source code of the toolkit that generates the leakage benchmark dataset, as well as the detection algorithms used, are released as open source.
Zenodo (CERN European Organization for Nuclear Research), Jul 22, 2022
Emergencies and disasters (such as earthquakes and floods), may contaminate drinking water system... more Emergencies and disasters (such as earthquakes and floods), may contaminate drinking water systems with pathogens, that can affect the health of both First Responders and Citizens. As part of the Horizon 2020 "Pathogen Contamination Emergency Response Technologies" (PathoCERT) project, we are developing a Digital Twin tool (PathoINVEST) to assist First Responders and Water Authorities in investigating and responding efficiently to drinking water contamination events. In this paper, we present preliminary work on PathoINVEST, its architecture, and how it operates with the PathoCERT ecosystem of technologies. Moreover, using an illustrative case study, we demonstrate how PathoINVEST will process data and produce useful insights for the First Responders during a realistic contamination event. This work demonstrates how different research results can be integrated into a holistic water contamination emergency management system, in accordance with the needs of First Responders who need to make decisions within a limited time frame and to reduce the impact of a contamination event.
Journal of Water Resources Planning and Management
A key challenge in designing algorithms for leakage detection and isolation in drinking water dis... more A key challenge in designing algorithms for leakage detection and isolation in drinking water distribution systems, is the performance evaluation and comparison between methodologies using benchmarks. For this purpose, the "Battle of the Leakage Detection and Isolation Methods" (Bat-tLeDIM) competition was organized in 2020 with the aim to objectively compare the performance of methods for the detection and localization of leakage events, relying on SCADA measurements of flow and pressure sensors installed within a virtual water distribution system. Several teams from academia and the industry submitted their solutions, using various techniques including time-series analysis, statistical methods, machine learning, mathematical programming, meta-heuristics and engineering judgment, and were evaluated using realistic economic criteria. This paper summarizes the results of the competition and conducts an analysis of the different leakage detection and isolation methods used by the teams. The competition results highlight the need for further development of methods for leakage detection and isolation, and also the need to develop additional open benchmark problems for this purpose.
This dataset contains a TIF file for Cyprus elevations. The file comes to merge all small areas T... more This dataset contains a TIF file for Cyprus elevations. The file comes to merge all small areas TIF files from the Department of Lands and Surveys and relates to altitude data. The digital terrain model is a homogeneous and regular point grid, which shows altitude on the ground, to create a model of the earth's surface.<br> <br> Source: https://bit.ly/3ovUhny
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Progress in IS, 2017
The critical infrastructures such as power distribution networks, water networks, transportation ... more The critical infrastructures such as power distribution networks, water networks, transportation and telecommunication networks that are settled within the area of a city produce a large amount of data from applications such as AMI, SCADA, Renewable Energy Management System, Asset Management System, Weather data, etc. To convert these massive data into useful information, visualization is an effective solution. Visualization is an established methodology in scientific community and it has been used in many fields because of its strong capability in large data management and information display. However, its applications in a holistic view of critical infrastructures mapping at a city level is a missing link or still in infancy stages for some specific infrastructures (e.g. water and power networks). In this paper, a technique to visualize critical infrastructure data by using a system that consists of GIS (Geographic Information System) for buffer spatial analysis and Google Earth for display is proposed. The goal of this work is to design, model and validate a benchmark system that is capable to visualize and map as well as to prepare the next interlinking phase of modeling interdependencies of several critical infrastructures such as distribution power networks, water networks, transportation networks and communication networks. The tool that will be used in achieving this goal is the open source QGIS. Further we aim to provide the grounds for a theoretical framework that can capture the interdependencies between critical infrastructures using techniques from graph theory, machine learning, econometric science and operation research. This framework is the first step in developing fundamental mechanisms for resilient management of critical infrastructures for the safe operation of smart cities.
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Drinking Water Distribution Networks (DWDN) are susceptible to infrastructure failures, which may... more Drinking Water Distribution Networks (DWDN) are susceptible to infrastructure failures, which may lead to water losses. Typically, these water losses are due to background leakages and pipe bursts which may occur anywhere within the distribution network. Background leakages are normally difficult to detect due to their small size, whereas pipe bursts are easier to locate as they are of larger size and may appear on the surface. The early detection and localization of some leakage event is extremely important, as this would reduce the time required for accommodating the event and therefore reducing the risk of further infrastructure degradation, contamination events and consumer complaints. In previous years, a number of methodologies have been proposed to detect and isolate the location of leakage events using various types of sensor measurements. These methods were commonly evaluated on private commercial datasets, and as a result, it is not possible to objectively compare these me...
The increase of streaming data from water utilities is enabling the development of real-time anom... more The increase of streaming data from water utilities is enabling the development of real-time anomaly and fault detection algorithms that can detect events, such as pipe bursts and leakages. Currently, there is not a widely accessible dataset of real or realistic leakage scenarios, which could be used as a common benchmark to compare different algorithms, as well as to support research reproducibility. In this work we propose the design of a realistic leakage dataset, the Leakage Diagnosis Benchmark (LeakDB). The dataset is comprised of a large number of artificially created but realistic leakage scenarios, on different water distribution networks, under varying conditions. Additionally, a scoring algorithm was developed in MATLAB to evaluate the results of different algorithms using various metrics. The usage of the LeakDB dataset, is demonstrated by scoring four detection algorithms. The dataset is stored on an open research data repository, and will be updated in the future with n...
This code is intended to supplement the paper submitted to the Journal of Water Resources Plannin... more This code is intended to supplement the paper submitted to the Journal of Water Resources Planning and Management titled: "Active Contamination Fault Detection in Water Distribution Systems". This is the second release which includes additional simulation results.
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Lecture Notes in Computer Science, 2016
This work describes a new open-source software platform, the dbpRisk software, for conducting sim... more This work describes a new open-source software platform, the dbpRisk software, for conducting simulation experiments in order to model the formation for disinfection by-product in drinking water distribution networks under various conditions and uncertainties. The goal is to identify the risk-level at each node location, contributing in the enhancement of consumer safety. The use of the dbpRisk software is demonstrated using a real water distribution network model from the Nicosia water transport network.
IFIP Advances in Information and Communication Technology, 2013
An intelligent building should take all the necessary steps to provide protection against the dis... more An intelligent building should take all the necessary steps to provide protection against the dispersion of contaminants from sources (events) inside the building which can compromise the indoor air quality and influence the occupants' comfort, health, productivity and safety. Multi-zone models and software, such as CONTAM, have been widely used in building environmental studies for predicting airflows and the resulting contaminant transport. This paper describes a developed Matlab Toolbox that allows the creation of data sets from running multiple scenarios using CONTAM by varying the different problem parameters. The Matlab-CONTAM Toolbox is an expandable research tool which facilitates the implementation of various algorithms related to contamination event monitoring. In particular, this paper describes the implementation of state-of-the-art algorithms for detecting and isolating a contaminant source. The use of the Toolbox is demonstrated through a building case-study. The Matlab-CONTAM Toolbox is released under an open-source licence, and is available at https://github.com/KIOS-Research/matlab-contam-toolbox.
Lecture Notes in Computer Science, 2013
In this work, we address the problem of airborne contaminant sensor placement in high-risk buildi... more In this work, we address the problem of airborne contaminant sensor placement in high-risk buildings where critical infrastructures are managed and operated, making them possible locations for terrorist attacks (such as governmental buildings and ministries, utilities, airports and hospitals). A new software is presented based on the "Matlab-CONTAM Toolbox" and the CONTAM multi-zone simulation software, to construct multiple scenarios of contamination events and to solve the multi-objective sensor placement problem for minimizing the average and maximum impact risk with respect to the contaminant mass inhaled impact metric. The use of the software is demonstrated in a case-study using the Holmes's House benchmark. The Toolbox is released under an open-source license at https://github.com/KIOS-Research/ matlab-contam-toolbox.
Procedia Engineering, 2014
This plugin enables users to open a window within the QGIS environment where the user can browse ... more This plugin enables users to open a window within the QGIS environment where the user can browse through the web. This can help the user to view for example tutorials and manuals for QGIS or any other web content.
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Papers by Marios Kyriakou