Adaptable urban water demand prediction system

2015 ◽  
Vol 15 (5) ◽  
pp. 958-964 ◽  
Author(s):  
G. Banjac ◽  
M. Vašak ◽  
M. Baotić
Keyword(s):  
Mutual Information ◽  
Water Demand ◽  
Distribution Systems ◽  
Water Distribution ◽  
Meteorological Data ◽  
Model Parameters ◽  
Prediction System ◽  
Prediction Ability ◽  
Demand Prediction ◽  
Water Demand Prediction

In this work, identification of 24-hours-ahead water demand prediction model based on historical water demand data is considered. As part of the identification procedure, the input variable selection algorithm based on partial mutual information is implemented. It is shown that meteorological data on a daily basis are not relevant for the water demand prediction in the sense of partial mutual information for the analysed water distribution systems of the cities of Tavira, Algarve, Portugal and Evanton East, Scotland, UK. Water demand prediction system is modelled using artificial neural networks, which offer a great potential for the identification of complex dynamic systems. The adaptive tuning procedure of model parameters is also developed in order to enable the model to adapt to changes in the system. A significant improvement of the prediction ability of such a model in relation to the model with fixed parameters is shown when a certain trend is present in the water demand profile.

scholarly journals Application of LSTM Networks for Water Demand Prediction in Optimal Pump Control

Water ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 644
Author(s):  
Christian Kühnert ◽  
Naga Mamatha Gonuguntla ◽  
Helene Krieg ◽  
Dimitri Nowak ◽  
Jorge A. Thomas
Keyword(s):  
Drinking Water ◽  
Water Demand ◽  
Water Distribution ◽  
Short Term Memory ◽  
Time Series Prediction ◽  
Real Life ◽  
Training Data ◽  
Demand Prediction ◽  
Additional Information ◽  
Water Demand Prediction

Every morning, water suppliers need to define their pump schedules for the next 24 h for drinking water production. Plans must be designed in such a way that drinking water is always available and the amount of unused drinking water pumped into the network is reduced. Therefore, operators must accurately estimate the next day’s water consumption profile. In real-life applications with standard consumption profiles, some expert system or vector autoregressive models are used. Still, in recent years, significant improvements for time series prediction have been achieved through special deep learning algorithms called long short-term memory (LSTM) networks. This paper investigates the applicability of LSTM models for water demand prediction and optimal pump control and compares LSTMs against other methods currently used by water suppliers. It is shown that LSTMs outperform other methods since they can easily integrate additional information like the day of the week or national holidays. Furthermore, the online- and transfer-learning capabilities of the LSTMs are investigated. It is shown that LSTMs only need a couple of days of training data to achieve reasonable results. As the focus of the paper is on the real-world application of LSTMs, data from two different water distribution plants are used for benchmarking. Finally, it is shown that the LSTMs significantly outperform the system currently in operation.

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

scholarly journals Modeling Bacterial Regrowth and Trihalomethane Formation in Water Distribution Systems

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 463
Author(s):  
Gopinathan R. Abhijith ◽  
Leonid Kadinski ◽  
Avi Ostfeld
Keyword(s):  
Distribution Systems ◽  
Water Distribution ◽  
Mechanistic Model ◽  
Water Distribution Systems ◽  
Sensitivity Study ◽  
Operating Conditions ◽  
Growth Rate Constant ◽  
Model Parameters ◽  
Bacterial Regrowth ◽  
The Impact

The formation of bacterial regrowth and disinfection by-products is ubiquitous in chlorinated water distribution systems (WDSs) operated with organic loads. A generic, easy-to-use mechanistic model describing the fundamental processes governing the interrelationship between chlorine, total organic carbon (TOC), and bacteria to analyze the spatiotemporal water quality variations in WDSs was developed using EPANET-MSX. The representation of multispecies reactions was simplified to minimize the interdependent model parameters. The physicochemical/biological processes that cannot be experimentally determined were neglected. The effects of source water characteristics and water residence time on controlling bacterial regrowth and Trihalomethane (THM) formation in two well-tested systems under chlorinated and non-chlorinated conditions were analyzed by applying the model. The results established that a 100% increase in the free chlorine concentration and a 50% reduction in the TOC at the source effectuated a 5.87 log scale decrement in the bacteriological activity at the expense of a 60% increase in THM formation. The sensitivity study showed the impact of the operating conditions and the network characteristics in determining parameter sensitivities to model outputs. The maximum specific growth rate constant for bulk phase bacteria was found to be the most sensitive parameter to the predicted bacterial regrowth.

scholarly journals Hydraulic Performance Analysis and Modeling of Water Supply Distribution System of Addis Ababa Science and Technology University, Ethiopia

2020 ◽  
Author(s):  
Chalchisa Milkecha ◽  
Habtamu Itefa
Keyword(s):  
Water Supply ◽  
Water Demand ◽  
Distribution System ◽  
Distribution Systems ◽  
Water Distribution ◽  
Addis Ababa ◽  
Science And Technology ◽  
Hydraulic Performance ◽  
Alternative Methods ◽  
The University

This study was conducted generally by aiming assessment of the hydraulic performance of water distribution systems of Addis Ababa Science and Technology University (AASTU). In line with the main objective, this study addressed, (1) pinpointing problems of existing water supply versus demand deficit (2) evaluating the hydraulic performance of water distribution system using water GEMS and (3) recommended alternative methods for improving water demand scenarios. The University’s water supply distribution network layout was a looped system and the flow of water derived by both gravity and pressurized system. The gravity flow served for the academic and administrative staffs whereas the pressurized system of the network fed the students dormitories, cafeteria’s etc. The study revealed the existence of unmet minimum pressure requirement around the student dormitories which accounts 25.64% below the country’s building code standard during the peak hour consumption. The result of the water demand projection showed an increment of 2.5 liter per capita demand (LPCD) in every five years. Hence, first, the university’s water demand was projected and then hydraulic parameters such as; pressure, head loss and velocity were modeled for both the existing and the improved water supply distribution. The finding of the study was recommended to the university’s water supply project and institutional development offices for its future modification and rehabilitation works.

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.

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