scholarly journals Spatial and temporal disaggregation of water demand and leakage of the water distribution network in Skiathos, Greece

2015 ◽  
Author(s):  
Chrysi Laspidou ◽  
Dimitris Kofinas ◽  
Nikolaos Mellios
Keyword(s):  
Water Demand ◽  
Water Distribution ◽  
Distribution Network ◽  
Water Distribution Network ◽  
Temporal Disaggregation

scholarly journals Multivariate statistical analysis for water demand modelling: implementation, performance analysis, and comparison with the PRP model

2015 ◽  
Vol 18 (1) ◽  
pp. 4-22 ◽  
Author(s):  
Chiara M. Fontanazza ◽  
Vincenza Notaro ◽  
Valeria Puleo ◽  
Gabriele Freni
Keyword(s):  
Statistical Analysis ◽  
Return Period ◽  
Water Demand ◽  
Distribution Systems ◽  
Water Distribution ◽  
Distribution Network ◽  
Water Distribution Network ◽  
Water Volume ◽  
Demand Model ◽  
Consumption Data

Water demand is the driving force behind hydraulic dynamics in water distribution systems. Consequently, it is crucial to accurately estimate the actual water use to develop reliable simulation models. In this study, copula-based multivariate analysis was proposed and used for demand prediction for a given return period. The analysis was applied to water consumption data collected in the water distribution network of Palermo (Italy). The approach produced consistent demand patterns and could be a powerful tool when coupled with water distribution network models for design or analysis problems. The results were compared with those obtained using a classical water demand model, the Poisson rectangular pulse (PRP) model. The multivariate consumption data statistical analysis results were always higher than those of the PRP model but the copula-based method maintained the daily water volume of actual consumptions and provided maximum daily consumption that increased with the return period.

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 Robust Design of a Real-Life Water Distribution Network under Different Demand Scenarios

Water ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 753
Author(s):  
Ina Vertommen ◽  
Karel van Laarhoven ◽  
Maria da Conceição Cunha
Keyword(s):  
Robust Design ◽  
Water Demand ◽  
Water Distribution ◽  
Demand Uncertainty ◽  
Distribution Network ◽  
Real Life ◽  
Water Distribution Network ◽  
Distribution Networks ◽  
Optimization Approach ◽  
And Performance

In this paper a scenario-based robust optimization approach is proposed to take demand uncertainty into account in the design of water distribution networks. This results in insight in the trade-off between costs and performance of different designs. Within the proposed approach the designer is able to choose the desired degree of risk aversion, and the performance of the design can be assessed based on the water demand effectively supplied under different scenarios. Both future water demand scenarios and scenarios based on historical records are considered. The approach is applied to the design of a real-life water distribution network supplying part of a city in the Netherlands. From the results the relation between costs and performance for different scenarios becomes evident: a more robust design requires higher design costs. Moreover, it is proven that numerical optimization helps finding better design solutions when compared to manual approaches. The developed approach allows water utilities to make informed choices about how much to invest in their infrastructure and how to design it in order to achieve a certain level of robustness.

scholarly journals WATER DEMAND PREDICTION USING ARTIFICIAL NEURAL NETWORK FOR SUPERVISORY CONTROL

2016 ◽  
Vol 36 (1) ◽  
pp. 148-154
Author(s):  
BI Gwaivangmin ◽  
JD Jiya
Keyword(s):  
Neural Network ◽  
Supervisory Control ◽  
Water Demand ◽  
Water Distribution ◽  
Distribution Network ◽  
Water Distribution Network ◽  
Optimal Operation ◽  
Demand Prediction ◽  
The Neural Network ◽  
Water Demand Prediction

With increase in population growth, industrial development and economic activities over the years, water demand could not be met in a water distribution network.  Thus, water demand forecasting becomes necessary at the demand nodes.  This paper presents Hourly water demand prediction at the demand nodes of a water distribution network using NeuNet Pro 2.3 neural network software and the monitoring and control of water distribution using supervisory control.  The case study is the Laminga Water Treatment Plant and its water distribution network, Jos.  The proposed model will be developed based on historic records of water demand in the 15 selected demand nodes for 60 days, 24 hours run. The data set is categorized into two set, one for training the neural network and the other for testing, with a learning rate of 50 and hidden nodes of 10 of the neural network model.  The prediction results revealed a satisfactory performance of the neural network prediction of the water demand. The predictions are then used for supervisory control to remotely control and monitor the hydraulic parameters of the water demand nodes. The practical application in the plant will cut down the cost of water production and even to a large extend provide optimal operation of the distribution networks solving the perennial problem of water scarcity in Jos. http://dx.doi.org/10.4314/njt.v36i1.19

EFFICIENT CHLORINATION SCHEDULE FOR A WATER DISTRIBUTION NETWORK WITH MULTIPLE PUMPING STATIONS

2017 ◽  
Vol 16 (5) ◽  
pp. 1071-1079 ◽  
Author(s):  
Andrei-Mugur Georgescu ◽  
Sanda-Carmen Georgescu ◽  
Remus Alexandru Madularea ◽  
Diana Maria Bucur ◽  
Georgiana Dunca
Keyword(s):  
Water Distribution ◽  
Distribution Network ◽  
Water Distribution Network ◽  
Pumping Stations

A study on appropriate investment of pipeline rehabilitation for water distribution network

2005 ◽  
Vol 5 (2) ◽  
pp. 31-38
Author(s):  
A. Asakura ◽  
A. Koizumi ◽  
O. Odanagi ◽  
H. Watanabe ◽  
T. Inakazu
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Water Supply ◽  
Water Distribution ◽  
Ad Hoc ◽  
Distribution Network ◽  
Water Distribution Network ◽  
Distribution Networks ◽  
Optimal Basis ◽  
Model Case

In Japan most of the water distribution networks were constructed during the 1960s to 1970s. Since these pipelines were used for a long period, pipeline rehabilitation is necessary to maintain water supply. Although investment for pipeline rehabilitation has to be planned in terms of cost-effectiveness, no standard method has been established because pipelines were replaced on emergency and ad hoc basis in the past. In this paper, a method to determine the maintenance of the water supply on an optimal basis with a fixed budget for a water distribution network is proposed. Firstly, a method to quantify the benefits of pipeline rehabilitation is examined. Secondly, two models using Integer Programming and Monte Carlo simulation to maximize the benefits of pipeline rehabilitation with limited budget were considered, and they are applied to a model case and a case study. Based on these studies, it is concluded that the Monte Carlo simulation model to calculate the appropriate investment for the pipeline rehabilitation planning is both convenient and practical.

Whole life costing: application to water distribution network

2003 ◽  
Vol 3 (1-2) ◽  
pp. 87-93 ◽  
Author(s):  
M. Engelhardt ◽  
D. Savic ◽  
P. Skipworth ◽  
A. Cashman ◽  
A. Saul ◽  
...  
Keyword(s):  
Network Management ◽  
Asset Management ◽  
Water Distribution ◽  
Distribution Network ◽  
Operating Cost ◽  
Water Distribution Network ◽  
Activity Based Costing ◽  
Operational Strategies ◽  
Long Term Impact ◽  
Whole Life Costing

There is an increasing pressure from the economic regulator in England and Wales for water companies to ensure that their capital maintenance decisions reflect an understanding of the long-term impact on their operational costs and risks. This implies that decisions must not only reflect the costs borne now but the likely costs in the future, and how these might be optimised. It is noteworthy that within the construction and transport industries, asset management decisions which have been driven in this direction utilise a whole life costing (WLC) methodology. This paper addresses the implications of transferring the concept of WLC to service-based assets such as water systems. A WLC approach to distribution network management aims to achieve the lowest network provision and operating cost when all costs are considered to achieve standards enforced by regulation. Cognisance is to be taken of all relevant costs - direct and indirect, private and societal - in order to balance the needs of the service supplier, the customer, society and the environment in a sustainable manner. A WLC analysis thus attempts to develop a cost profile over the life of the asset. Accounting for the costs over this period is achieved through a combination of activity based costing (ABC) and a life cycle assessment (LCA) used to identify potential social and environmental costs. This process means that each of these identified costs must be linked to some physical parameter that itself varies over time due to changing demands on the system, the different operational strategies available to the operator and natural deterioration of the fabric of the system. The links established between the cost and activities of the operator provide the basis for the development of a WLC decision tool (WiLCO) for application to water distribution network management.

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