scholarly journals Heuristic burst detection method using flow and pressure measurements

2014 ◽  
Vol 16 (5) ◽  
pp. 1194-1209 ◽  
Author(s):  
M. Bakker ◽  
J. H. G. Vreeburg ◽  
M. Van De Roer ◽  
L. C. Rietveld
Keyword(s):  
Water Flow ◽  
Water Demand ◽  
Distribution System ◽  
Detection Method ◽  
Dynamic Pressure ◽  
Demand Forecasting ◽  
Negative Consequences ◽  
Burst Detection ◽  
Water Losses ◽  
Expected Values

Pipe bursts in a drinking water distribution system lead to water losses, interruption of supply, and damage to streets and houses due to the uncontrolled water flow. To minimize the negative consequences of pipe bursts, an early detection is necessary. This paper describes a heuristic burst detection method, which continuously compares measured and expected values of water demands and pressures. The expected values of the water demand are generated by an adaptive water demand forecasting model, and the expected values of the pressures are generated by a dynamic pressure drop – demand relation estimator. The method was tested off-line on a historic dataset of 5 years of water flow and pressure data in three supply areas (with 650, 11,180 and 130,920 connections) in the western part of the Netherlands. In the period 274 bursts were reported of which, based on the definition we propose in this paper, 38 were considered as relatively larger bursts. The method was able to detect 50, 25.9 and 7.8% in the considered areas related to all bursts, and around 80% in all three areas related to the subset of relatively larger bursts. The method generated false alarms on 3% of the evaluated days on average.

Analysis of historic bursts and burst detection in water supply areas of different size

2014 ◽  
Vol 14 (6) ◽  
pp. 1035-1044 ◽  
Author(s):  
M. Bakker ◽  
E. A. Trietsch ◽  
J. H. G. Vreeburg ◽  
L. C. Rietveld
Keyword(s):  
Urban Environment ◽  
Water Distribution ◽  
Detection Method ◽  
Burst Size ◽  
Distribution Networks ◽  
Negative Consequences ◽  
Burst Detection ◽  
Water Losses ◽  
Customer Contact ◽  
Water Company

Pipe bursts in water distribution networks lead to water losses and a risk of damaging the urban environment. We studied hydraulic data and customer contact records of 44 real bursts for a better understanding of the phenomena. We found that most bursts were reported to the water company shortly after the beginning, and the negative consequences of the bursts were limited. However, smaller bursts that stayed unnoticed for a longer time period or larger bursts that began in the late evening or in the night were problematic to the water company that had no burst detection method installed. Detection of those bursts was critical to minimise the negative consequences, and a burst detection method could perform this task. We studied the relation between the size of supply area and the size of the bursts that can be detected. Therefore, we applied a heuristic burst detection method on historic datasets of eight areas varying in size between 1,500 and 48,300 connections. We found a correlation between the size of the area and the minimum detectable burst size and quickly detectable burst size. To reduce the risk of substantial water losses or damage to the urban environment, the burst detection method can effectively be applied to areas with an average demand of 150 m3/h or less.

scholarly journals Water Demand Forecasting using Deep Learning in IoT Enabled Water Distribution Network

2020 ◽  
Vol 15 (6) ◽  
Author(s):  
Lakshmi Kanthan Narayanan ◽  
Suresh Sankaranarayanan ◽  
Joel J P C Rodrigues ◽  
Sergei Kozlov
Keyword(s):  
Deep Learning ◽  
Water Demand ◽  
Distribution System ◽  
Water Distribution ◽  
Short Term Memory ◽  
Water Distribution System ◽  
Distribution Network ◽  
Demand Forecasting ◽  
Water Distribution Network ◽  
Water Losses

Most of the water losses occur during water distribution in pipelines during transportation. In order to eradicate the losses, an “IoT based water distribution system” integrated with “Fog and Cloud Computing" proposed for water distribution and underground health monitoring of pipes. For developing an effective water distribution system based on Internet of Things (IoT), the demand of the consumer should be analysed. So, towards predicting the water demand for consumers, Deep learning methodology called Long Short-Term Memory (LSTM) is compared with traditional Time Series methodology called Auto Regressive Integrated Moving Average (ARIMA) in terms of error and accuracy. Now based on demand prediction with higher accuracy, an IoT integrated “Water Distribution Network (WDN)” is designed using hydraulic engineering. This WDN design will ensure minimal losses during transportation and quality of water to the consumers. This will lead to development of a smart system for water distribution.

scholarly journals Real-Time Burst Detection in District Metering Areas in Water Distribution System Based on Patterns of Water Demand with Supervised Learning

Water ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 1765 ◽  
Author(s):  
Pingjie Huang ◽  
Naifu Zhu ◽  
Dibo Hou ◽  
Jinyu Chen ◽  
Yao Xiao ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Water Demand ◽  
Distribution System ◽  
Distribution Systems ◽  
Water Distribution ◽  
Time Window ◽  
Learning Algorithm ◽  
Burst Detection ◽  
Pipe Burst ◽  
Dynamic Time

This paper proposes a new method to detect bursts in District Metering Areas (DMAs) in water distribution systems. The methodology is divided into three steps. Firstly, Dynamic Time Warping was applied to study the similarity of daily water demand, extract different patterns of water demand, and remove abnormal patterns. In the second stage, according to different water demand patterns, a supervised learning algorithm was adopted for burst detection, which established a leakage identification model for each period of time, respectively, using a sliding time window. Finally, the detection process was performed by calculating the abnormal probability of flow during a certain period by the model and identifying whether a burst occurred according to the set threshold. The method was validated on a case study involving a DMA with engineered pipe-burst events. The results obtained demonstrate that the proposed method can effectively detect bursts, with a low false-alarm rate and high accuracy.

scholarly journals Estimating High Resolution Temporal Scale of Water Demand Time Series – Disaggregation Approach (Case Study)

10.29007/4vfl ◽  
2018 ◽  
Author(s):  
Peyman Yousefi ◽  
Gholamreza Naser ◽  
Hadi Mohammadi
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Water Demand ◽  
Distribution System ◽  
Urban Areas ◽  
Water Distribution ◽  
Demand Forecasting ◽  
Temporal Scale ◽  
A New Technique ◽  
Cascade Method

A comprehensive understanding of water demand and its availability is essential for decision-makers to manage their resources and understand related risks effectively. Historical data play a crucial role in developing an integrated plan for management of water distribution system. The key is to provide high-resolution temporal-scale of demand data in urban areas. In the literature, many studies on water demand forecasting are available; most of them were focused on monthly-scales. Since monitoring of time series is a prolonged and costly procedure, the popularity of disaggregation methods is a most recent desirable trend. The objective of this research is to transfer low-resolution into high-resolution temporal scale using random cascade disaggregation and non-linear deterministic methods. This study defines a new technique to apply previously proposed random cascade method to disaggregate continuous data of the city of Peachland. The accuracy of the results is more than 90%. It represents a satisfactory application of the models. The proposed approach helps operators to have access to daily demand without acquiring high-resolution temporal scale values. Although the disaggregated values may not be precisely equal with observed values, it offers a practical solution for the low equipped WDS and leads to lesser number of drinking water-related problems.

scholarly journals Monitoring Nonrevenue Water Performance in Intermittent Supply

Water ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 1220 ◽  
Author(s):  
Taha AL-Washali ◽  
Saroj Sharma ◽  
Fadhl AL-Nozaily ◽  
Mansour Haidera ◽  
Maria Kennedy
Keyword(s):  
Regression Analysis ◽  
Performance Indicators ◽  
Water Demand ◽  
Distribution System ◽  
Water System ◽  
Water Utilities ◽  
Water Losses ◽  
Individual System ◽  
System Input ◽  
Supply Time

Water utilities should monitor their nonrevenue water (NRW) levels properly to manage water losses and sustain water services. However, monitoring NRW is problematic in an intermittent water supply regime. This is because more supplied water to users imposes higher volumes of NRW, and supplying significantly less water results in an unmet water demand but interestingly less NRW. This study investigates the influence of the amount of water supplied to a distribution system on the reported level of NRW. The volume and indicators of NRW all vary with variations in the system input volume (SIV). This is even more critical for monitoring NRW for systems shifting from intermittent to continuous supply. To enable meaningful monitoring, the NRW volume should be normalised. Addressing that, this research proposes two normalisation approaches: regression analysis and average supply time adjustment. Analysis of the NRW performance indicators showed that regression analysis enables the monitoring of NRW and tracking its progression in an individual system only, but not for a comparison with other systems. For comparing (or benchmarking) a water system to other systems with different supply patterns, the average supply time adjustment should be used. However, this approach presents significant uncertainties when the average supply time is less than eight hours per day.

scholarly journals Real-Time Dynamic Hydraulic Model of Water Distribution Networks

Water ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 470 ◽  
Author(s):  
Adnan Abu-Mahfouz ◽  
Yskandar Hamam ◽  
Philip Page ◽  
Kazeem Adedeji ◽  
Amos Anele ◽  
...  
Keyword(s):  
Real Time ◽  
Water Demand ◽  
Demand Forecasting ◽  
Hydraulic Model ◽  
Leakage Detection ◽  
Pressure Management ◽  
Model Framework ◽  
Water Losses ◽  
Time Dynamic ◽  
Background Leakage

The impact of climate change and increasing urbanisation throughout the world has forced water utility managers to increase the efficiency of water resources. Reduction of real (or physical) water losses plays a crucial role in improving the efficiency of water supply systems. Considering these challenges, it will not be enough to rely only on traditional approaches to solve the problem of water losses. Therefore, more advanced techniques need to be developed and utilized. Recently, a framework for a real-time dynamic hydraulic model for potable water loss reduction was proposed. This paper focuses mainly on the three major components of the proposed real-time dynamic hydraulic model framework for potable water loss reduction, which have been developed recently. These are background leakage detection, pressure management, and water demand forecasting. A background leakage detection algorithm was proposed which, amongst others, permits the localisation of potential critical nodes or pipes with higher leakage flow in the network where such pressure management could be performed. More so, new controllers (algorithms) which perform pressure management by accurately setting the pressure, using either a pressure control valve or variable speed pump, have been constructed. In addition, background leakage flow is greatly affected by demand variations, a water demand forecasting model is constructed with the aim of annexing the demand variation for multi-period leakage analysis. Thus, a short-term water demand forecast utilising the Model Conditional Processor was constructed to forecast the following hour demand and the associated predictive uncertainty. Although each of these components have been tested independently, future work is ongoing for merging these components and integration within the dynamic hydraulic model framework.

scholarly journals IoT-based water demand forecasting and distribution design for smart city

2019 ◽  
Vol 11 (4) ◽  
pp. 1411-1428 ◽  
Author(s):  
Lakshmi Kanthan Narayanan ◽  
Suresh Sankaranarayanan
Keyword(s):  
Water Demand ◽  
Distribution System ◽  
Smart City ◽  
Water Distribution ◽  
Water Distribution System ◽  
Moving Average ◽  
Demand Forecasting ◽  
Underground Water ◽  
Daily Basis ◽  
Water Demand Forecasting

Abstract The percentage of fresh water resource availability in the world is diminishing every year. According to a world economic forum survey, the increase in water demand will result in high scarcity globally in the next two decades. The eradication of the water demand increase and reducing the losses during the transportation of water is challenging. Thus accordingly, an Internet of Things (IoT)-based architecture integrated with Fog for underground water distribution system has been proposed. Towards designing an IoT water distribution architecture for a smart city, we need to first forecast the water demand for consumers. Hence, accordingly, water demand forecasting has been carried out on a daily basis for a period of three months as a case study using autoregressive integrated moving average (ARIMA) and regression analysis. Based on water demand forecasting analysis, a water distribution design for an IoT-based architecture has been carried out using hydraulic engineering design for proper distribution of water with minimal losses which would result in the development of a smart water distribution system (SWDS). This has been carried out using EPANET.

scholarly journals Burst Detection by Water Demand Nowcasting Based on Exogenous Sensors

2021 ◽  
Author(s):  
Caspar V. C. Geelen ◽  
Doekle R. Yntema ◽  
Jaap Molenaar ◽  
Karel J. Keesman
Keyword(s):  
Drinking Water ◽  
Water Demand ◽  
False Positive ◽  
Historical Data ◽  
False Positive Rate ◽  
Demand Forecasting ◽  
Pressure Sensors ◽  
Burst Detection ◽  
Water Demand Forecasting ◽  
Positive Rate

AbstractBursts of drinking water pipes not only cause loss of drinking water, but also damage below and above ground infrastructure. Short-term water demand forecasting is a valuable tool in burst detection, as deviations between the forecast and actual water demand may indicate a new burst. Many of burst detection methods struggle with false positives due to non-seasonal water consumption as a result of e.g. environmental, economic or demographic exogenous influences, such as weather, holidays, festivities or pandemics. Finding a robust alternative that reduces the false positive rate of burst detection and does not rely on data from exogenous processes is essential. We present such a burst detection method, based on Bayesian ridge regression and Random Sample Consensus. Our exogenous nowcasting method relies on signals of all nearby flow and pressure sensors in the distribution net with the aim to reduce the false positive rate. The method requires neither data of exogenous processes, nor extensive historical data, but only requires one week of historical data per flow/pressure sensor. The exogenous nowcasting method is compared with a common water demand forecasting method for burst detection and shows sufficiently higher Nash-Sutcliffe model efficiencies of 82.7% - 90.6% compared to 57.9% - 77.7%, respectively. These efficiency ranges indicate a more accurate water demand prediction, resulting in more precise burst detection.

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