Enhanced Water Leak Detection with Convolutional Neural Networks and One-Class Support Vector Machine

• URL: http://arxiv.org/abs/2511.11650v1
• Date: Mon, 10 Nov 2025 14:33:29 GMT
• Title: Enhanced Water Leak Detection with Convolutional Neural Networks and One-Class Support Vector Machine
• Authors: Daniele Ugo Leonzio, Paolo Bestagini, Marco Marcon, Stefano Tubaro,
• Abstract summary: A new method for leak detection is proposed in this paper.<n>The method is based on water pressure measurements acquired at a series of nodes of a Water Distribution Networks (WDNs)<n>The proposed solution is based on a one-class Support Vector Machines (SVM) trained on no-leak data, so that leaks are detected as anomalies.
• Score: 22.98836082046212
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Water is a critical resource that must be managed efficiently. However, a substantial amount of water is lost each year due to leaks in Water Distribution Networks (WDNs). This underscores the need for reliable and effective leak detection and localization systems. In recent years, various solutions have been proposed, with data-driven approaches gaining increasing attention due to their superior performance. In this paper, we propose a new method for leak detection. The method is based on water pressure measurements acquired at a series of nodes of a WDN. Our technique is a fully data-driven solution that makes only use of the knowledge of the WDN topology, and a series of pressure data acquisitions obtained in absence of leaks. The proposed solution is based on an feature extractor and a one-class Support Vector Machines (SVM) trained on no-leak data, so that leaks are detected as anomalies. The results achieved on a simulate dataset using the Modena WDN demonstrate that the proposed solution outperforms recent methods for leak detection.

Related papers

• AI-Driven Forecasting and Monitoring of Urban Water System [5.652933022735071]
  Underground water and wastewater pipelines are vital for city operations but plagued by anomalies like leaks and infiltrations.<n>In recent years, artificial intelligence has advanced rapidly and is increasingly applied to urban infrastructure.<n>We propose an integrated AI and remote-sensor framework to address the challenge of leak detection in underground water pipelines.
  arXiv  Detail & Related papers  (2025-10-08T04:28:38Z)
• Filling in the Blanks: Applying Data Imputation in incomplete Water Metering Data [0.0]
  We study the application of recent data imputation techniques to enhance monitoring and management of water distribution networks using smart water meters.<n>Our results indicate that effective data imputation can substantially enhance the quality of the insights derived from water consumption data.
  arXiv  Detail & Related papers  (2025-06-10T15:13:05Z)
• Dual Unscented Kalman Filter Architecture for Sensor Fusion in Water Networks Leak Localization [2.423124553011487]
  This article proposes a hydraulic state estimation methodology based on a dual Unscented Kalman Filter (UKF) approach.<n>The strategy is evaluated in well-known open source case studies, namely Modena and L-TOWN.
  arXiv  Detail & Related papers  (2024-12-16T12:01:08Z)
• Underwater Object Detection in the Era of Artificial Intelligence: Current, Challenge, and Future [119.88454942558485]
  Underwater object detection (UOD) aims to identify and localise objects in underwater images or videos. In recent years, artificial intelligence (AI) based methods, especially deep learning methods, have shown promising performance in UOD.
  arXiv  Detail & Related papers  (2024-10-08T00:25:33Z)
• Simple Ingredients for Offline Reinforcement Learning [86.1988266277766]
  offline reinforcement learning algorithms have proven effective on datasets highly connected to the target downstream task. We show that existing methods struggle with diverse data: their performance considerably deteriorates as data collected for related but different tasks is simply added to the offline buffer. We show that scale, more than algorithmic considerations, is the key factor influencing performance.
  arXiv  Detail & Related papers  (2024-03-19T18:57:53Z)
• Investigating the Suitability of Concept Drift Detection for Detecting Leakages in Water Distribution Networks [7.0072935721154614]
  Leakages are a major risk in water distribution networks as they cause water loss and increase contamination risks. Leakage detection is a difficult task due to the complex dynamics of water distribution networks. From a machine-learning perspective, leakages can be modeled as concept drift.
  arXiv  Detail & Related papers  (2024-01-03T13:12:04Z)
• Innovative Horizons in Aerial Imagery: LSKNet Meets DiffusionDet for Advanced Object Detection [55.2480439325792]
  We present an in-depth evaluation of an object detection model that integrates the LSKNet backbone with the DiffusionDet head. The proposed model achieves a mean average precision (MAP) of approximately 45.7%, which is a significant improvement. This advancement underscores the effectiveness of the proposed modifications and sets a new benchmark in aerial image analysis.
  arXiv  Detail & Related papers  (2023-11-21T19:49:13Z)
• Estimating irregular water demands with physics-informed machine learning to inform leakage detection [0.0]
  Leakages in drinking water distribution networks pose significant challenges to water utilities. We present a physics-informed machine learning algorithm that analyses pressure data and estimates unknown irregular water demands.
  arXiv  Detail & Related papers  (2023-09-06T11:55:16Z)
• Small Object Detection via Coarse-to-fine Proposal Generation and Imitation Learning [52.06176253457522]
  We propose a two-stage framework tailored for small object detection based on the Coarse-to-fine pipeline and Feature Imitation learning. CFINet achieves state-of-the-art performance on the large-scale small object detection benchmarks, SODA-D and SODA-A.
  arXiv  Detail & Related papers  (2023-08-18T13:13:09Z)
• Watermarking for Out-of-distribution Detection [76.20630986010114]
  Out-of-distribution (OOD) detection aims to identify OOD data based on representations extracted from well-trained deep models. We propose a general methodology named watermarking in this paper. We learn a unified pattern that is superimposed onto features of original data, and the model's detection capability is largely boosted after watermarking.
  arXiv  Detail & Related papers  (2022-10-27T06:12:32Z)
• Contextual-Bandit Anomaly Detection for IoT Data in Distributed Hierarchical Edge Computing [65.78881372074983]
  IoT devices can hardly afford complex deep neural networks (DNN) models, and offloading anomaly detection tasks to the cloud incurs long delay. We propose and build a demo for an adaptive anomaly detection approach for distributed hierarchical edge computing (HEC) systems. We show that our proposed approach significantly reduces detection delay without sacrificing accuracy, as compared to offloading detection tasks to the cloud.
  arXiv  Detail & Related papers  (2020-04-15T06:13:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.