| 1. |
- Sousa, Diego Perdigão, et al.
(författare)
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Leakage detection in water distribution networks using machine-learning strategies
- 2023
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Ingår i: Water Science and Technology. - : IWA Publishing. - 1606-9749 .- 1607-0798. ; 23:3, s. 1115-1126
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Tidskriftsartikel (refereegranskat)abstract
- This work proposes a reliable leakage detection methodology for water distribution networks (WDNs) using machine-learning strategies. Our solution aims at detecting leakage in WDNs using efficient machine-learning strategies. We analyze pressure measurements from pumps in district metered areas (DMAs) in Stockholm, Sweden, where we consider a residential DMA of the water distribution network. Our proposed methodology uses learning strategies from unsupervised learning (K-means and cluster validation techniques), and supervised learning (learning vector quantization algorithms). The learning strategies we propose have low complexity, and the numerical experiments show the potential of using machine-learning strategies in leakage detection for monitored WDNs. Specifically, our experiments show that the proposed learning strategies are able to obtain correct classification rates up to 93.98%.
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| 2. |
- Commowick, Olivier, et al.
(författare)
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Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure
- 2018
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Ingår i: Scientific Reports. - : Nature Publishing Group. - 2045-2322. ; 8
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Tidskriftsartikel (refereegranskat)abstract
- We present a study of multiple sclerosis segmentation algorithms conducted at the international MICCAI 2016 challenge. This challenge was operated using a new open-science computing infrastructure. This allowed for the automatic and independent evaluation of a large range of algorithms in a fair and completely automatic manner. This computing infrastructure was used to evaluate thirteen methods of MS lesions segmentation, exploring a broad range of state-of-theart algorithms, against a high-quality database of 53 MS cases coming from four centers following a common definition of the acquisition protocol. Each case was annotated manually by an unprecedented number of seven different experts. Results of the challenge highlighted that automatic algorithms, including the recent machine learning methods (random forests, deep learning,.), are still trailing human expertise on both detection and delineation criteria. In addition, we demonstrate that computing a statistically robust consensus of the algorithms performs closer to human expertise on one score (segmentation) although still trailing on detection scores.
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| 3. |
- Perdigão Sousa, Diego, et al.
(författare)
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A Federated Prototype-Based Model for IoT Systems: A Study Case for Leakage Detection in a Real Water Distribution Network
- 2024
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Ingår i: Wireless Sensor Networks in Smart Environments: Enabling Digitalization. - : Wiley-IEEE Press.
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Bokkapitel (refereegranskat)abstract
- This work proposes a reliable leakage detection analysis for water distribution networks (WDNs) by combining efficient and emergent machine learning techniques. In this study case, we analyze pressure and flow measurements from pumps in Stockholm, Sweden, where we consider a residential district metered area of the WDN. Our solution aims at detecting leakage in WDNs using a prototype-based model while preserving data privacy by proposing a federated learning approach. The machine learning strategies we adopt have low complexity, and the numerical experiments show the potential of using federated prototype-based techniques for leakage detection on monitored WDNs. Specifically, our experiments show that the proposed learning method can obtain higher detection rates at each pumping station than the conventional centralized approach, e.g., improvements of purity rates up to 7.6% in one of the pumping stations, which increased the minimum values from 92.13%, obtained through centralized learning, to 99.11%, obtained via federated learning.
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| 4. |
- Sousa, Diego Perdigão, et al.
(författare)
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Leakage Detection In Water Distribution Networks : Efficient Training By Data Clustering
- 2022
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Ingår i: IWA World Water Congress & Exhibition, Sep. 2022. - : IWA Publishing.
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Konferensbidrag (refereegranskat)abstract
- This work proposes a reliable leakage detection methodology for water distribution networks based on machine learning techniques. The design is developed through real data acquisition from a municipal area of a water distribution network. We propose to combine both unsupervised learning (K-means and cluster validation techniques) and supervised learning (LVQ-type algorithms) for the efficient design of prototype-based classifiers. We investigated several metrics aiming to define the optimal number of clusters, in which we succeeded in reporting attractive classification accuracies (approximately 90%) on scenarios of severely limited number of prototypes.
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