Estimating irregular water demands with physics-informed machine learning to inform leakage detection

• URL: http://arxiv.org/abs/2309.02935v1
• Date: Wed, 6 Sep 2023 11:55:16 GMT
• Title: Estimating irregular water demands with physics-informed machine learning to inform leakage detection
• Authors: Ivo Daniel and Andrea Cominola
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Leakages in drinking water distribution networks pose significant challenges to water utilities, leading to infrastructure failure, operational disruptions, environmental hazards, property damage, and economic losses. The timely identification and accurate localisation of such leakages is paramount for utilities to mitigate these unwanted effects. However, implementation of algorithms for leakage detection is limited in practice by requirements of either hydraulic models or large amounts of training data. Physics-informed machine learning can utilise hydraulic information thereby circumventing both limitations. In this work, we present a physics-informed machine learning algorithm that analyses pressure data and therefrom estimates unknown irregular water demands via a fully connected neural network, ultimately leveraging the Bernoulli equation and effectively linearising the leakage detection problem. Our algorithm is tested on data from the L-Town benchmark network, and results indicate a good capability for estimating most irregular demands, with R2 larger than 0.8. Identification results for leakages under the presence of irregular demands could be improved by a factor of 5.3 for abrupt leaks and a factor of 3.0 for incipient leaks when compared the results disregarding irregular demands.

Related papers

• A Little Leak Will Sink a Great Ship: Survey of Transparency for Large Language Models from Start to Finish [47.3916421056009]
  Large Language Models (LLMs) are trained on massive web-crawled corpora. LLMs produce leaked information in most cases despite less such data in their training set. Self-detection method showed superior performance compared to existing detection methods.
  arXiv  Detail & Related papers  (2024-03-24T13:21:58Z)
• Localizing Anomalies in Critical Infrastructure using Model-Based Drift Explanations [5.319765271848658]
  We analyze the effects of anomalies on the dynamics of critical infrastructure systems by modeling the networks employing Bayesian networks. In particular, we argue that model-based explanations of concept drift are a promising tool for localizing anomalies. To showcase that our methodology applies to critical infrastructure more generally, we showcase the suitability of the derived technique to localize sensor faults in power systems.
  arXiv  Detail & Related papers  (2023-10-24T13:33:19Z)
• MFL Data Preprocessing and CNN-based Oil Pipeline Defects Detection [0.0]
  Application of computer vision for anomaly detection has been under attention in several industrial fields. This work focuses on the research of the Magnetic Flux Leakage data and the preprocessing techniques. In doing so, we exploited the recent convolutional neural network structures and proposed robust approaches.
  arXiv  Detail & Related papers  (2023-09-30T10:37:12Z)
• Censored Deep Reinforcement Patrolling with Information Criterion for Monitoring Large Water Resources using Autonomous Surface Vehicles [0.0]
  This work proposes a framework for monitoring large water resources with autonomous vehicles. Using information gain as a measure of the uncertainty reduction over data, it is proposed a Deep Q-Learning algorithm improved by a Q-Censoring mechanism for model-based obstacle avoidance.
  arXiv  Detail & Related papers  (2022-10-12T07:33:46Z)
• Ranking-Based Physics-Informed Line Failure Detection in Power Grids [66.0797334582536]
  Real-time and accurate detecting of potential line failures is the first step to mitigating the extreme weather impact and activating emergency controls. Power balance equations nonlinearity, increased uncertainty in generation during extreme events, and lack of grid observability compromise the efficiency of traditional data-driven failure detection methods. This paper proposes a Physics-InformEd Line failure Detector (FIELD) that leverages grid topology information to reduce sample and time complexities and improve localization accuracy.
  arXiv  Detail & Related papers  (2022-08-31T18:19:25Z)
• Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure management [64.17887333976593]
  Avoiding over-pressurization in subsurface reservoirs is critical for applications like CO2 sequestration and wastewater injection. Managing the pressures by controlling injection/extraction are challenging because of complex heterogeneity in the subsurface. We use differentiable programming with a full-physics model and machine learning to determine the fluid extraction rates that prevent over-pressurization.
  arXiv  Detail & Related papers  (2022-06-21T20:38:13Z)
• Lidar Light Scattering Augmentation (LISA): Physics-based Simulation of Adverse Weather Conditions for 3D Object Detection [60.89616629421904]
  Lidar-based object detectors are critical parts of the 3D perception pipeline in autonomous navigation systems such as self-driving cars. They are sensitive to adverse weather conditions such as rain, snow and fog due to reduced signal-to-noise ratio (SNR) and signal-to-background ratio (SBR)
  arXiv  Detail & Related papers  (2021-07-14T21:10:47Z)
• Prediction of Hydraulic Blockage at Cross Drainage Structures using Regression Analysis [11.532200478443773]
  This paper proposes to use machine learning regression analysis for the prediction of hydraulic blockage. With deployment of hydraulic sensors in smart cities, and availability of Big Data, regression analysis may prove helpful in addressing the blockage detection problem.
  arXiv  Detail & Related papers  (2021-03-06T04:15:25Z)
• Predictive Analytics for Water Asset Management: Machine Learning and Survival Analysis [55.41644538483948]
  We study a statistical and machine learning framework for the prediction of water pipe failures. We use a dataset containing the failure records of all pipes within the water distribution network in Barcelona, Spain. The results shed light on the effect of important risk factors, such as pipe geometry, age, material, and soil cover, among others.
  arXiv  Detail & Related papers  (2020-07-02T19:08:36Z)
• Why Normalizing Flows Fail to Detect Out-of-Distribution Data [51.552870594221865]
  Normalizing flows fail to distinguish between in- and out-of-distribution data. We demonstrate that flows learn local pixel correlations and generic image-to-latent-space transformations. We show that by modifying the architecture of flow coupling layers we can bias the flow towards learning the semantic structure of the target data.
  arXiv  Detail & Related papers  (2020-06-15T17:00:01Z)
• A Multi-step and Resilient Predictive Q-learning Algorithm for IoT with Human Operators in the Loop: A Case Study in Water Supply Networks [4.9550706407171585]
  We consider the problem of recommending resilient and predictive actions for an IoT network in the presence of faulty components. We use anonymized data from Arlington County, Virginia, to compute predictive and resilient scheduling policies for a smart water supply system.
  arXiv  Detail & Related papers  (2020-06-06T15:51:52Z)

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.