scholarly journals Data reconstruction of flow time series in water distribution systems – a new method that accommodates multiple seasonality

2016 ◽  
Vol 19 (2) ◽  
pp. 238-250 ◽  
Author(s):  
Rui Barrela ◽  
Conceição Amado ◽  
Dália Loureiro ◽  
Aisha Mamade
Keyword(s):  
Time Series ◽  
Network Flow ◽  
Distribution Systems ◽  
Water Distribution ◽  
Missing Values ◽  
Window Size ◽  
Flow Time ◽  
New Method ◽  
Data Reconstruction ◽  
Flow Data

The purpose of this paper is to present a simple yet highly effective method to reconstruct missing data in flow time series. The presence of missing values in network flow data severely restricts their use for an adequate management of billing systems and for network operation. Despite significant technology improvements, missing values are frequent due to metering, data acquisition and storage issues. The proposed method is based on a weighted function for forecast and backcast obtained from existing time series models that accommodate multiple seasonality. A comprehensive set of tests were run to demonstrate the effectiveness of this new method and results indicated that a model for flow data reconstruction should incorporate daily and seasonal components for more accurate predictions, the window size used for forecast and backcast should range between 1 and 4 weeks, and the use of two disjoint training sets to generate flow predictions is more robust to detect anomalous events than other existing methods. Results obtained for flow data reconstruction provide evidence of the effectiveness of the proposed approach.

scholarly journals WDSchain: A Toolbox for Enhancing the Security Using Blockchain Technology in Water Distribution System

Water ◽  
2021 ◽  
Vol 13 (14) ◽  
pp. 1944
Author(s):  
Haitham H. Mahmoud ◽  
Wenyan Wu ◽  
Yonghao Wang
Keyword(s):  
Time Series ◽  
Distribution Systems ◽  
Water Distribution ◽  
Time Series Data ◽  
Series Data ◽  
Process Time ◽  
Time Interval ◽  
Security Level ◽  
Data Validation ◽  
Simulated Time

This work develops a toolbox called WDSchain on MATLAB that can simulate blockchain on water distribution systems (WDS). WDSchain can import data from Excel and EPANET water modelling software. It extends the EPANET to enable simulation blockchain of the hydraulic data at any intended nodes. Using WDSchain will strengthen network automation and the security in WDS. WDSchain can process time-series data with two simulation modes: (1) static blockchain, which takes a snapshot of one-time interval data of all nodes in WDS as input and output into chained blocks at a time, and (2) dynamic blockchain, which takes all simulated time-series data of all the nodes as input and establishes chained blocks at the simulated time. Five consensus mechanisms are developed in WDSchain to provide data at different security levels using PoW, PoT, PoV, PoA, and PoAuth. Five different sizes of WDS are simulated in WDSchain for performance evaluation. The results show that a trade-off is needed between the system complexity and security level for data validation. The WDSchain provides a methodology to further explore the data validation using Blockchain to WDS. The limitations of WDSchain do not consider selection of blockchain nodes and broadcasting delay compared to commercial blockchain platforms.

scholarly journals Minimum Night Flow Analysis and Application of the Fixed and Variable Area Discharges Model for Characterizing Leakage in the Gorino Ferrarese (FE-Italy) District

2020 ◽  
Vol 2 (1) ◽  
pp. 8
Author(s):  
Irene Marzola ◽  
Stefano Alvisi ◽  
Marco Franchini
Keyword(s):  
Time Series ◽  
Distribution Systems ◽  
Water Distribution ◽  
Flow Analysis ◽  
Water Utilities ◽  
Smart Meters ◽  
Minimum Night Flow ◽  
Major Interest ◽  
The Relationship ◽  
District Metered Area

Leakage in water distribution systems is an important issue and of major interest for water utilities. In this study, the Minimum Night Flow (MNF) method to quantify the amount of water lost and the equations representing the relationship between pressure and leakage in power and FAVAD (Fixed and Variable Area Discharge) forms were applied to a District Metered Area (DMA) located in Gorino Ferrarese (FE, Italy) equipped with smart meters. The analysis carried out by exploiting the collected time series of user water consumption, DMA inflow, and pressure highlighted that: (a) the MNF method can lead to significant inaccuracy in leakage estimation in the presence of users with irregular consumptions, when based on literature values, and (b) the estimation of the parameters of the power and FAVAD equation is highly affected by the number and types of observed data used.

Event Detection in Water Distribution Systems from Multivariate Water Quality Time Series

2012 ◽  
Vol 46 (15) ◽  
pp. 8212-8219 ◽  
Author(s):  
Lina Perelman ◽  
Jonathan Arad ◽  
Mashor Housh ◽  
Avi Ostfeld
Keyword(s):  
Water Quality ◽  
Time Series ◽  
Event Detection ◽  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution Systems

scholarly journals Experiments based comparative evaluations of machine learning techniques for leak detection in water distribution systems

2021 ◽  
Author(s):  
Maryam Kammoun ◽  
Amina Kammoun ◽  
Mohamed Abid
Keyword(s):  
Machine Learning ◽  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution Systems ◽  
Leak Detection ◽  
Distribution Networks ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Flow Data ◽  
Learning Techniques

Abstract Leakage in water distribution systems is a significant long-standing problem due to the huge economic and ecological losses. Different leak detection studies have been examined in literature using different types of technologies and data. Currently, although machine learning techniques have achieved tremendous progress in outlier detection approaches, they are still limited in terms of water leak detection applications. This research aims to improve the leak detection performances by refining the choices of learning data and techniques. From this perspective, commonly used techniques for leak detection are assessed in this paper, and the characteristics of hydraulic data are investigated. Four intelligent algorithms are compared, namely k-nearest neighbors, support vector machines, logistic regression, and multi-layer perceptron. This study focuses on six experiments based on identifying outliers in various packages of pressure and flow data, yearly data, seasonal data, night data, and flow data difference to detect leakage in water distribution networks. Different scenarios of realistic water demand in two networks from the benchmark dataset LeakDB are used. Results demonstrate that the leak detection accuracy varies between 30% and 100% depending on the experiment and the choices of algorithms and data.

scholarly journals Analysis of MNF and FAVAD Models for Leakage Characterization by Exploiting Smart-Metered Data: The Case of the Gorino Ferrarese (FE-Italy) District

Water ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 643
Author(s):  
Irene Marzola ◽  
Stefano Alvisi ◽  
Marco Franchini
Keyword(s):  
Time Series ◽  
Water Balance ◽  
Water Consumption ◽  
Distribution Systems ◽  
Water Distribution ◽  
Water Utilities ◽  
Time Step ◽  
Minimum Night Flow ◽  
The Relationship ◽  
District Metered Area

Leakages in water distribution systems have great economic and environmental impacts and are a major issue for water utilities. In this work, the water balance and the Minimum Night Flow (MNF) method for evaluating the amount of water loss, as well as the power and Fixed and Variable Area Discharge (FAVAD) equations for analyzing the relationship between leakage and pressure, were applied to a fully monitored District Metered Area (DMA) located in Gorino Ferrarese (FE, Italy). Time series of (a) the water consumption of each user, (b) the DMA inflow, and (c) the pressure at the DMA inlet point were monitored with a 5 min time step. The results of an analysis carried out by exploiting the collected time series highlighted that: (a) The application of the MNF method based on literature values can lead to significant inaccuracies in the presence of users with irregular consumption, and (b) the estimation of the parameters of the power and FAVAD equations is highly affected by the amounts and types of observed data used.

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