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 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 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 A bottom-up approach of stochastic demand allocation in water quality modelling

2010 ◽  
Vol 3 (1) ◽  
pp. 43-51 ◽  
Author(s):  
E. J. M. Blokker ◽  
J. H. G. Vreeburg ◽  
H. Beverloo ◽  
M. Klein Arfman ◽  
J. C. van Dijk
Keyword(s):  
Drinking Water ◽  
Water Demand ◽  
Distribution System ◽  
Water Distribution ◽  
Water Distribution System ◽  
Drinking Water Distribution System ◽  
Travel Times ◽  
Bottom Up ◽  
Drinking Water Distribution ◽  
Demand Patterns

Abstract. An "all pipes" hydraulic model of a drinking water distribution system was constructed with two types of demand allocations. One is constructed with the conventional top-down approach, i.e. a demand multiplier pattern from the booster station is allocated to all demand nodes with a correction factor to account for the average water demand on that node. The other is constructed with a bottom-up approach of demand allocation, i.e., each individual home is represented by one demand node with its own stochastic water demand pattern. This was done for a drinking water distribution system of approximately 10 km of mains and serving ca. 1000 homes. The system was tested in a real life situation. The stochastic water demand patterns were constructed with the end-use model SIMDEUM on a per second basis and per individual home. Before applying the demand patterns in a network model, some temporal aggregation was done. The flow entering the test area was measured and a tracer test with sodium chloride was performed to determine travel times. The two models were validated on the total sum of demands and on travel times. The study showed that the bottom-up approach leads to realistic water demand patterns and travel times, without the need for any flow measurements or calibration. In the periphery of the drinking water distribution system it is not possible to calibrate models on pressure, because head losses are too low. The study shows that in the periphery it is also difficult to calibrate on water quality (e.g. with tracer measurements), as a consequence of the high variability between days. The stochastic approach of hydraulic modelling gives insight into the variability of travel times as an added feature beyond the conventional way of modelling.

scholarly journals An Innovative Hourly Water Demand Forecasting Preprocessing Framework with Local Outlier Correction and Adaptive Decomposition Techniques

Water ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 582 ◽  
Author(s):  
Shiyuan Hu ◽  
Jinliang Gao ◽  
Dan Zhong ◽  
Liqun Deng ◽  
Chenhao Ou ◽  
...  
Keyword(s):  
Water Demand ◽  
Water Distribution ◽  
Demand Forecasting ◽  
Cost Effective ◽  
Distribution Networks ◽  
Correction Method ◽  
Support Vector ◽  
Forecasting Models ◽  
Water Demand Forecasting ◽  
Local Outlier

Accurate forecasting of hourly water demand is essential for effective and sustainable operation, and the cost-effective management of water distribution networks. Unlike monthly or yearly water demand, hourly water demand has more fluctuations and is easily affected by short-term abnormal events. An effective preprocessing method is needed to capture the hourly water demand patterns and eliminate the interference of abnormal data. In this study, an innovative preprocessing framework, including a novel local outlier detection and correction method Isolation Forest (IF), an adaptive signal decomposition technique Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), and basic forecasting models have been developed. In order to compare a promising deep learning method Gated Recurrent Unit (GRU) as a basic forecasting model with the conventional forecasting models, Support Vector Regression (SVR) and Artificial Neural Network (ANN) have been used. The results show that the proposed hybrid method can utilize the complementary advantages of the preprocessing methods to improve the accuracy of the forecasting models. The root-mean-square error of the SVR, ANN, and GRU models has been reduced by 57.5%, 27.8%, and 30.0%, respectively. Further, the GRU-based models developed in this study are superior to the other models, and the IF-CEEMDAN-GRU model has the highest accuracy. Hence, it is promising that this preprocessing framework can improve the performance of the water demand forecasting models.

scholarly journals Correlation Analysis of Water Demand and Predictive Variables for Short-Term Forecasting Models

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
B. M. Brentan ◽  
G. Meirelles ◽  
M. Herrera ◽  
E. Luvizotto ◽  
J. Izquierdo
Keyword(s):  
Water Demand ◽  
Distribution Systems ◽  
Water Distribution ◽  
Demand Forecasting ◽  
Principal Component ◽  
Correlation Study ◽  
Self Organizing Maps ◽  
Water Demand Forecasting ◽  
Predictive Variables ◽  
Short Term Forecasting

Operational and economic aspects of water distribution make water demand forecasting paramount for water distribution systems (WDSs) management. However, water demand introduces high levels of uncertainty in WDS hydraulic models. As a result, there is growing interest in developing accurate methodologies for water demand forecasting. Several mathematical models can serve this purpose. One crucial aspect is the use of suitable predictive variables. The most used predictive variables involve weather and social aspects. To improve the interrelation knowledge between water demand and various predictive variables, this study applies three algorithms, namely, classical Principal Component Analysis (PCA) and machine learning powerful algorithms such as Self-Organizing Maps (SOMs) and Random Forest (RF). We show that these last algorithms help corroborate the results found by PCA, while they are able to unveil hidden features for PCA, due to their ability to cope with nonlinearities. This paper presents a correlation study of three district metered areas (DMAs) from Franca, a Brazilian city, exploring weather and social variables to improve the knowledge of residential demand for water. For the three DMAs, temperature, relative humidity, and hour of the day appear to be the most important predictive variables to build an accurate regression model.

Real Time Water Demand Estimation in Water Distribution System

2008 ◽  
Author(s):  
Feng Shang ◽  
James G. Uber ◽  
Bart G. van Bloemen Waanders ◽  
Dominic Boccelli ◽  
Robert Janke
Keyword(s):  
Real Time ◽  
Water Demand ◽  
Distribution System ◽  
Water Distribution ◽  
Water Distribution System ◽  
Demand Estimation

scholarly journals Performance evaluation of a municipal water distribution system using WaterCAD and Epanet

2018 ◽  
Vol 8 (3) ◽  
pp. 459-467 ◽  
Author(s):  
J. C. Agunwamba ◽  
O. R. Ekwule ◽  
C. C. Nnaji
Keyword(s):  
Water Demand ◽  
Distribution System ◽  
Water Distribution ◽  
Statistical Difference ◽  
Water Distribution System ◽  
Head Loss ◽  
Water Supply Systems ◽  
Water Conveyance ◽  
Negative Pressures ◽  
Supply Systems

Abstract Population explosion in urban settings usually exerts enormous pressure on existing water supply systems. The result is that overall water demand is usually not satisfied. This study evaluated the performance of Wadata sub-zone water distribution system with respect to pressure, velocity, hydraulic head loss and nodal demands using WaterCAD and Epanet. There was no statistical difference between the results of Epanet and WaterCAD, however, Epanet produced slightly higher results of pressure and velocity in about 60% of all cases examined. About 19 percent (18.52%) of the total number of nodes analyzed had negative pressures while 69 percent (69%) of the nodes had pressures less than the adopted pressure for the analysis. These negative pressures indicate that there is inadequate head within the distribution network for water conveyance to all the sections. About 88 percent (87.7%) of flow velocities in the pipes were within the adopted velocity while around 12 percent (12.3%) of the velocities exceeded the adopted velocity. These excess velocities are partly responsible for the leakages and pipe bursts observed at some points within the system. The results in this study revealed that the performance of the water distribution system of Wadata sub-zone under current demand is inefficient.

Sign in / Sign up

Export Citation Format

Share Document