scholarly journals Development of Leakage Detection Model and Its Application for Water Distribution Networks Using RNN-LSTM

2021 ◽  
Vol 13 (16) ◽  
pp. 9262
Author(s):  
Chan-Wook Lee ◽  
Do-Guen Yoo
Keyword(s):  
Deep Learning ◽  
Water Supply ◽  
Leak Detection ◽  
Distribution Networks ◽  
Research Topic ◽  
Current Level ◽  
Analysis Tool ◽  
Leakage Detection ◽  
Detection Model ◽  
Challenging Research

With the advent of the 4th Industrial Revolution, advanced measurement infrastructure and utilization technologies are being noticeably introduced into the water supply system to store and utilize measurement data. From this perspective, the leak detection technology in water supply networks is becoming increasingly vital to sustainable water resource management and the clean water supply worldwide. In particular, leakage detection of buried pipelines is rated as a very challenging research topic given the current level of technology. However, leakage in buried underground pipelines is rated as a very challenging research topic given the current level of technology. Therefore, a data-driven leak detection model was developed through this study using deep learning technology based on inflow meter data. Multiple threshold-based models were applied to reduce the RNN-LSTM (Recurrent Neural Networks–Long Short-Term Memory models) deep learning and false prediction range, which is programmed in conjunction with the Python language and Google Colaboratory (a big data analysis tool). The developed model consists of flow pattern shape extraction, RNN-LSTM-based flow prediction, and threshold setting modules. The developed model was applied to the actual leakage accident data, followed by the performance evaluation. As a result, the leak was recognized at most points immediately after the accident. The performance of leak detection was evaluated by a Confusion matrix and showed more than 90% accuracy at all points except singularities. Therefore, the developed model can be used as a critical software technology to proactively identify various at present with smart water infrastructure being introduced. In addition, this model is highly scalable as it can consider various operational situations based on the expert system, and it can also efficiently reflect the results of pipe network analysis across different scenarios.

scholarly journals Multiple Leak Detection in Water Distribution Networks Following Seismic Damage

2021 ◽  
Vol 13 (15) ◽  
pp. 8306
Author(s):  
Jeongwook Choi ◽  
Gimoon Jeong ◽  
Doosun Kang
Keyword(s):  
Water Distribution ◽  
Large Scale ◽  
Detection Efficiency ◽  
Leak Detection ◽  
Distribution Networks ◽  
Seismic Damage ◽  
Water Distribution Networks ◽  
Detection Methods ◽  
Economic Losses ◽  
Detection Model

Water pipe leaks due to seismic damage are more difficult to detect than bursts, and such leaks, if not repaired in a timely manner, can eventually reduce supply pressure and generate both pollutant penetration risks and economic losses. Therefore, leaks must be promptly identified, and damaged pipes must be replaced or repaired. Leak-detection using equipment in the field is accurate; however, it is a considerably labor-intensive process that necessitates expensive equipment. Therefore, indirect leak detection methods applicable before fieldwork are necessary. In this study, a computer-based, multiple-leak-detection model is developed. The proposed technique uses observational data, such as the pressure and flow rate, in conjunction with an optimization method and hydraulic analysis simulations, to improve detection efficiency (DE) for multiple leaks in the field. A novel approach is proposed, i.e., use of a cascade and iteration search algorithms to effectively detect multiple leaks (with the unknown locations, quantities, and sizes encountered in real-world situations) due to large-scale disasters, such as earthquakes. This method is verified through application to small block-scale water distribution networks (WDNs), and the DE is analyzed. The proposed detection model can be used for efficient leak detection and the repair of WDNs following earthquakes.

scholarly journals Automatic Leak Detection in Buried Plastic Pipes of Water Supply Networks by Means of Vibration Measurements

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Alberto Martini ◽  
Marco Troncossi ◽  
Alessandro Rivola
Keyword(s):  
Water Supply ◽  
Leak Detection ◽  
Distribution Networks ◽  
Vibration Monitoring ◽  
Supply Networks ◽  
Water Pipes ◽  
Water Supply Networks ◽  
Experimental Campaign ◽  
Acceleration Signals

The implementation of strategies for controlling water leaks is essential in order to reduce losses affecting distribution networks of drinking water. This paper focuses on leak detection by using vibration monitoring techniques. The long-term goal is the development of a system for automatic early detection of burst leaks in service pipes. An experimental campaign was started to measure vibrations transmitted along water pipes by real burst leaks occurring in actual water supply networks. The first experimental data were used for assessing the leak detection performance of a prototypal algorithm based on the calculation of the standard deviation of acceleration signals. The experimental campaign is here described and discussed. The proposed algorithm, enhanced by means of proper signal filtering techniques, was successfully tested on all monitored leaks, thus proving effective for leak detection purpose.

scholarly journals Improvising Weakly Supervised Object Detection (WSOD) using Deep Learning Technique

2020 ◽  
Vol 9 (3) ◽  
pp. 728-732
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Object Detection ◽  
Detection Model ◽  
Challenging Research ◽  
Learning Technique ◽  
Computer Vision Technology ◽  
Model Training ◽  
Weakly Supervised ◽  
Near Future

Object detection is closely related with video and image analysis. Under computer vision technology, object detection model training with image-level labels only is challenging research area.Researchers have not yet discovered accurate model for Weakly Supervised Object Detection (WSOD). WSOD is used for detecting and localizing the objects under the supervision of image level annotations only.The proposed work usesself-paced approach which is applied on region proposal network of Faster R-CNN architecture which gives better solution from previous weakly-supervised object detectors and it can be applied for computer visionapplications in near future.

scholarly journals Pressure and Velocity Measure in the Pipeline for Leak Detection using COMSOL Multiphysics

2020 ◽  
Vol 9 (1) ◽  
pp. 1812-1816
Keyword(s):  
Laminar Flow ◽  
Water Supply ◽  
Distribution Network ◽  
Leak Detection ◽  
Comsol Multiphysics ◽  
Leakage Detection ◽  
Velocity Measure

COMSOL Multiphysics software is multipurpose software which can be used in every field. COMSOL Multiphysics to have less work in India. I have to review many papers on COMSOL but all the papers out of India. I have decided to work on COMSOL Multiphysics. This paper presents laminar flow inside the pipeline to calculate velocity and pressure to using valve in open and close case along the pipeline. I have research on leakage detection in water supply distribution network. For this purpose I have designed a multiple size pipes and multiple size valves in AutoCAD software.

An innovative machine learning based framework for water distribution network leakage detection and localization

2021 ◽  
pp. 147592172110402
Author(s):  
Xudong Fan ◽  
Xiong (Bill) Yu
Keyword(s):  
Machine Learning ◽  
Water Distribution ◽  
Technical Problem ◽  
Water Distribution Network ◽  
Economic Loss ◽  
Leak Detection ◽  
Distribution Networks ◽  
Detection Accuracy ◽  
Leakage Detection ◽  
Detection And Localization

Leakages in the underground water distribution networks (WDNs) waste over 1 billion gallon of water annually in the US and cause significant socio-economic loss to our communities. However, detecting and localization leakage in a WDN remains a challenging technical problem despite of significant progresses in this domain. The progresses in machine learning (ML) provides new ways to identify the leakage by data-driven methods. However, in-service WDNs are short of labeled data under leaking conditions, which makes it infeasible to use common ML models. This study proposed a novel machine learning (ML)-based framework for WDN leak detection and localization. This new framework, named clustering-then-localization semi-supervised learning (CtL-SSL), uses the topological relationship of WDN and its leakage characteristics for WDN partition and sensors placement, and subsequently utilizes the monitoring data for leakage detection and leakage localization. The CtL-SSL framework is applied to two testbed WDNs and achieves 95% leakage detection accuracy and around 83% final leakage localization accuracy by use of unbalanced data with less than 10% leaking data. The developed CtL-SSL framework advances the leak detection strategy by alleviating the data requirements, guiding optimal sensor placement, and locating leakage via WDN leakage zone partition. It features excellent scalability, extensibility, and upgradeability for applications to various types of WDNs. It will provide valuable a tool in sustainable management of the WDNs.

Application of hydraulic transients for leak detection in water supply systems

2004 ◽  
Vol 4 (5-6) ◽  
pp. 365-374 ◽  
Author(s):  
D. Covas ◽  
H. Ramos ◽  
N. Graham ◽  
C. Maksimovic
Keyword(s):  
Water Supply ◽  
Transient Analysis ◽  
Leak Detection ◽  
Detection Methods ◽  
Water Supply Systems ◽  
Analysis Tool ◽  
Monitoring And Control ◽  
Transient Event ◽  
Leak Location ◽  
Supply Systems

The current paper reports the investigation of two transient-based techniques for leak detection in water pipe systems using physical data collected in the laboratory and in quasi-field conditions. The first is the analysis of the leak reflected wave during a transient event and the second is inverse transient analysis (ITA). This was approached through the development of an inverse transient analysis tool and the collection of transient data for the testing and validation of this model. Two experimental programmes were carried out at Imperial College and in cooperation with Thames Water for the validation and testing of these techniques. Evaluation of the presence, location and size of leaks was carried out using the collected data. Transient-based techniques have been shown to be successful in the detection and location of leaks and leak location uncertainties depended on the leak size and location, flow regime and location where the transient event was generated. These leak detection methods are very promising for identifying the general area of the trunk main with leakage, and can be combined with other leak location techniques (e.g. acoustic equipment) to more precisely pinpoint the leak position. Transient-based techniques are particularly important for the diagnosis, monitoring and control of existing water supply systems, not only to detect leaks, but also to better understand the causes of pipe bursts and accidents, particularly when these are due to natural transient events.

Recognition of classroom student state features based on deep learning algorithms and machine learning

2020 ◽  
pp. 1-12
Author(s):  
Hu Jingchao ◽  
Haiying Zhang
Keyword(s):  
Deep Learning ◽  
Feature Recognition ◽  
Subjective Evaluation ◽  
Recognition Algorithm ◽  
Video Data ◽  
State Recognition ◽  
Detection Model ◽  
State Classification ◽  
Intelligent Models ◽  
Model Recognition

The difficulty in class student state recognition is how to make feature judgments based on student facial expressions and movement state. At present, some intelligent models are not accurate in class student state recognition. In order to improve the model recognition effect, this study builds a two-level state detection framework based on deep learning and HMM feature recognition algorithm, and expands it as a multi-level detection model through a reasonable state classification method. In addition, this study selects continuous HMM or deep learning to reflect the dynamic generation characteristics of fatigue, and designs random human fatigue recognition experiments to complete the collection and preprocessing of EEG data, facial video data, and subjective evaluation data of classroom students. In addition to this, this study discretizes the feature indicators and builds a student state recognition model. Finally, the performance of the algorithm proposed in this paper is analyzed through experiments. The research results show that the algorithm proposed in this paper has certain advantages over the traditional algorithm in the recognition of classroom student state features.

Analisis Hidrolis dan Jejak Karbon Jaringan Distribusi Air Bersih di Pulau Kecil Padat Penduduk (Pulau Lengkang Kecil, Kota Batam)

2020 ◽  
Vol 21 (2) ◽  
pp. 227-235
Author(s):  
Muhammad Rizki Apritama ◽  
I Wayan Koko Suryawan ◽  
Yosef Adicita
Keyword(s):  
Water Supply ◽  
Distribution System ◽  
Water Distribution ◽  
Treatment Plant ◽  
Primary Source ◽  
Distribution Networks ◽  
Clean Water ◽  
Island Community ◽  
Dug Wells ◽  
Centralized Wastewater Treatment

ABSTRACTThe clean water supply system network on Lengkang Kecil Island was developed in 2019. A small portion of the community's freshwater comes from harvesting rainwater and dug wells, which are only obtained during the rainy season. The primary source of clean water used by the community comes from underwater pipelines with a daily discharge of 0.86 l/sec. The water supply of the Lengkang Kecil Island community is 74.3 m3/day, with 146 House Connections (HCs) and to serve public facilities such as elementary schools, primary health centers, and mosques. Hydraulic evaluation of clean water distribution using EPANET 2.0 software on flow velocity shows the lowest rate of 0.29 m/s and the highest of 1.21 m/s. The lowest pressure value in the distribution system is 6.94-6.96 m and headloss units in the range 0.08-0.25 m/km. These three criteria are still within the distribution network design criteria (feasible). A carbon footprint can be calculated from each activity from the analysis of the evaluation of clean water distribution networks. The most massive emissions came from pumping activities with 131 kg CO2-eq, followed by emissions from wastewater 62.5 kgCO2-eq. Further research is needed to determine the quality of wastewater and the design for a centralized wastewater treatment plant (IPALT) to improve Lengkang Kecil Island residents' living standards.Keywords: Lengkang Kecil Island, water, EPANET, carbon footprintABSTRAKJaringan sistem penyediaan air bersih pada Pulau Lengkang Kecil dimulai pada tahun 2019. Sebagian kecil air bersih yang digunakan masyarakat berasal dari pemanenan air hujan dan sumur gali yang hanya didapat pada musim hujan. Sumber air bersih utama yang digunakan masyarakat berasal dari pengaliran perpipaan bawah laut dengan debit harian 0,86 l/detik. Kebutuhan air masyarakat Pulau Lengkang Kecil adalah 74,3 m3/hari dengan 146 Sambungan Rumah (SR) serta untuk melayani fasilitas umum seperti sekolah dasar (SD), puskesmas, dan masjid. Evaluasi hidrolis distribusi air bersih dengan menggunakan software EPANET 2.0 terhadap kriteria kecepatan aliran menunjukkan nilai terendah 0,29 m/s dan tertinggi 1,21 m/s. Nilai sisa tekan dalam sistem distribusi adalah 6,94–6,96 m dan unit headloss pada kisaran 0,08–0,25 m/km. Ketiga kriteria ini masih berada dalam kriteria desain jaringan distribusi (layak). Dari analisis evaluasi jaringan distribusi air bersih, dapat dihitung jejak karbon yang dihasilkan dari setiap kegiatannya. Emisi terbesar berasal dari kegiatan pemompaan dengan nilai 131 kgCO2-eq, diikuti dengan emisi yang berasal dari air limbah dengan nilai 62,5 kgCO2-eq. Penelitian lanjutan diperlukan untuk mengetahui kualitas dari air limbah dan desain untuk instalasi pengolahan air limbah terpusat (IPALT) untuk meningkatkan taraf hidup penduduk Pulau Lengkang Kecil.Kata kunci: Pulau Lengkang Kecil, air, EPANET, jejak karbon

Character Reorganization and Categorization using Hybrid Algorithms by Scanned Documents

2019 ◽  
Vol 8 (6S2) ◽  
pp. 943-948
Keyword(s):  
Research Work ◽  
Hybrid Algorithms ◽  
Research Topic ◽  
Image Processing Techniques ◽  
Digital Books ◽  
Otsu Method ◽  
Challenging Research ◽  
Processing Techniques ◽  
Scanned Documents ◽  
Day By Day

Detection and reorganization of text may save a lot of time while reproducing old books text and its chapters. This is really challenging research topic as different books may have different font types and styles. The digital books and eBooks reading habit is increasing day by day and new documents are producing every day. So in order to boost the process the text reorganization using digital image processing techniques can be used. This research work is using hybrid algorithms and morphological algorithms. For sample we have taken an letter pad where the text and images are separated using algorithms. The another objective of this research is to increase the accuracy of recognized text and produce accurate results. This research worked on two different concepts, first is concept of Pixel-level thresholding processing and another one is Otsu Method thresholding.

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