A neural network approach to burst detection

2002 ◽  
Vol 45 (4-5) ◽  
pp. 237-246 ◽  
Author(s):  
S.R. Mounce ◽  
A.J. Day ◽  
A.S. Wood ◽  
A. Khan ◽  
P.D. Widdop ◽  
...  
Keyword(s):  
Neural Network ◽  
Distribution System ◽  
Distribution Systems ◽  
Water Distribution ◽  
Time Series Data ◽  
Quality Data ◽  
Series Data ◽  
Neural Network Approach ◽  
Water Quality Data ◽  
Artificial Neural

This paper describes how hydraulic and water quality data from a distribution network may be used to provide a more efficient leakage management capability for the water industry. The research presented concerns the application of artificial neural networks to the issue of detection and location of leakage in treated water distribution systems. An architecture for an Artificial Neural Network (ANN) based system is outlined. The neural network uses time series data produced by sensors to directly construct an empirical model for predication and classification of leaks. Results are presented using data from an experimental site in Yorkshire Water's Keighley distribution system.

scholarly journals Pattern matching and associative artificial neural networks for water distribution system time series data analysis

2013 ◽  
Vol 16 (3) ◽  
pp. 617-632 ◽  
Author(s):  
S. R. Mounce ◽  
R. B. Mounce ◽  
T. Jackson ◽  
J. Austin ◽  
J. B. Boxall
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Water Quality ◽  
Time Series ◽  
Pattern Matching ◽  
Distribution Systems ◽  
Water Distribution ◽  
Quality Data ◽  
Series Data ◽  
Artificial Neural

Water distribution systems, and other infrastructures, are increasingly being pervaded by sensing technologies, collecting a growing volume of data aimed at supporting operational and investment decisions. These sensors monitor system characteristics, i.e. flows, pressures and water quality, such as in pipes. This paper presents the application of pattern matching techniques and binary associative neural networks for novelty detection in such data. A protocol for applying pattern matching to automatically recognise specific waveforms in time series based on their shapes is described together with a system called Advanced Uncertain Reasoning Architecture (AURA) Alert for autonomous determination of novelty. AURA is a class of binary neural network that has a number of advantages over standard artificial neural network techniques for condition monitoring including a sound theoretical basis to determine the bounds of the system operation. Results from application to several case studies are provided including both hydraulic and water quality data. In the case of pattern matching, the results demonstrated some transferability of burst patterns across District Metered Areas; however limitations in performance and difficulties with assembling pattern libraries were found. Results for the AURA system demonstrate the potential for robust event detection across multiple parameters providing valuable information for diagnosis; one example also demonstrates the potential for detection of precursor information, vital for proactive management.

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.

Dynamic prediction of Indian stock market: an artificial neural network approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Himanshu Goel ◽  
Narinder Pal Singh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stock Market ◽  
Institutional Investors ◽  
Time Series Data ◽  
Gradient Algorithm ◽  
Series Data ◽  
Content Type ◽  
Artificial Neural ◽  
Indian Stock Market

Purpose Artificial neural network (ANN) is a powerful technique to forecast the time series data such as the stock market. Therefore, this study aims to predict the Indian stock market closing price using ANNs. Design/methodology/approach The input variables identified from the literature are some macroeconomic variables and a global stock market factor. The study uses an ANN with Scaled Conjugate Gradient Algorithm (SCG) to forecast the Bombay Stock Exchange (BSE) Sensex. Findings The empirical findings reveal that the ANN model is able to achieve 93% accuracy in predicting the BSE Sensex closing prices. Moreover, the results indicate that the Morgan Stanley Capital International world index is the most important variable and the index of industrial production is the least important in predicting Sensex. Research limitations/implications The findings of the study have implications for the investors of all categories such as foreign institutional investors, domestic institutional investors and investment houses. Originality/value The novelty of this study lies in the fact that there are hardly any studies that use ANN to forecast the Indian stock market using macroeconomic indicators.

scholarly journals Construction and Application of Recognition Model for Black-Odorous Water Bodies Based on Artificial Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Zhonghua Xu ◽  
Changguo Dai ◽  
Jing Wang ◽  
Lejun Liu ◽  
Lei Jiang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Resource Management ◽  
Water Environment ◽  
Water Bodies ◽  
Quality Data ◽  
Water Quality Data ◽  
Recognition Model ◽  
Digital Twins ◽  
Artificial Neural

In the water environment, construction, and civil engineering industries, digital twins have gradually become a popular solution in recent years, and in digital twins, accurate data prediction and category recognition are important parts of it. Artificial neural network (ANN), a widely used data-driven model, can accurately identify nonlinear relationships in the water environment. In this paper, a recognition model for black-odorous water bodies based on ANN was established to directly identify the sensory description of water bodies. This study used water quality data and sensory description (color and odor) as samples to train backpropagation (BP) neural networks. The training results show that the accuracy of the color and odor models reaches 86.7% and 85.8%, respectively. It can thus be suggested that the sensory description can be accurately recognized by BP neural network. The application results indicate that all seven rivers had black-odorous phenomenon within a year. The recognition models have been instrumental in water resource management. Meanwhile, the models provide a reference for the evaluation and early warning of black-odorous water bodies in other regions.

A Neural Network Approach for Modeling Water Distribution System

2017 ◽  
Author(s):  
Andrei S. Popa ◽  
Conor O'Toole ◽  
Juan Munoz ◽  
Steve Cassidy ◽  
Dallas Tubbs ◽  
...  
Keyword(s):  
Neural Network ◽  
Distribution System ◽  
Water Distribution ◽  
Water Distribution System ◽  
Network Approach ◽  
Neural Network Approach

International Real Estate Review

2004 ◽  
Vol 7 (1) ◽  
pp. 121-138
Author(s):  
Xin J. Ge ◽  
◽  
G. Runeson ◽  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Hong Kong ◽  
Time Series Data ◽  
Series Data ◽  
Residential Property ◽  
Neural Network Approach ◽  
Price Performance ◽  
The Neural Network ◽  
Property Price

This paper develops a forecasting model of residential property prices for Hong Kong using an artificial neural network approach. Quarterly time-series data are applied for testing and the empirical results suggest that property price index, lagged one period, rental index, and the number of agreements for sales and purchases of units are the major determinants of the residential property price performance in Hong Kong. The results also suggest that the neural network methodology has the ability to learn, generalize, and converge time series.

Multi-objective optimization for conjunctive placement of hydraulic and water quality sensors in water distribution systems

2011 ◽  
Vol 11 (2) ◽  
pp. 166-171 ◽  
Author(s):  
Ami Preis ◽  
Andrew Whittle ◽  
Avi Ostfeld
Keyword(s):  
Water Quality ◽  
Sensor Network ◽  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution Systems ◽  
Quality Data ◽  
Hydraulic Pressure ◽  
Water Quality Data ◽  
Multi Objective ◽  
Water Quality Sensors

Near real-time continuous monitoring systems have been proposed as a promising approach for enhancing drinking water utilities detect and respond efficiently to threats on water distribution systems. Water quality sensors are aimed at revealing contamination intrusions, while hydraulic pressure and flow sensors are utilized for estimating the hydraulic system state. To date optimization models for placing sensors in water distribution systems are targeting separately water quality and hydraulic sensor network goals. Deploying two independent sensor networks within one distribution system is expensive to install and maintain. It might thus be beneficial to consider mutual sensor locations having dual hydraulic and water quality monitoring capabilities (i.e. sensor nodes which collect both hydraulic and water quality data at the same locations). In this study a multi-objective sensor network placement model for conjunctive monitoring of hydraulic and water quality data is developed and demonstrated using the multi-objective non-dominated sorted genetic algorithm NSGA II methodology. Two water distribution systems of increasing complexity are explored showing tradeoffs between hydraulic and water quality sensor location objectives. The proposed method provides a new tool for sensor placements.

scholarly journals Forecasting Rice Production of Bangladesh Using ARIMA and Artificial Neural Network Models

2020 ◽  
Vol 68 (2) ◽  
pp. 143-147
Author(s):  
Abira Sultana ◽  
Murshida Khanam
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Time Series Data ◽  
Arima Model ◽  
Rice Production ◽  
Series Data ◽  
Ann Model ◽  
Neural Network Models ◽  
Artificial Neural ◽  
Estimated Error

Forecasting behavior of Econometric and Machine Learning models has recently attracted much attention in the research sector. In this study an attempt has been made to compare the forecasting behavior of Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) using univariate time series data of annual rice production (1972 to 2013) of Bangladesh. Here, suitable ARIMA has been chosen from several selected ARIMA models with the help of AIC and BIC values. A simple ANN model using backpropagation algorithm with appropriate number of nodes or neurons in a single hidden layer, adjustable threshold value and learning rate, has been constructed. Based on the RMSE, MAE and MAPE values, the results showed that the estimated error of ANN is much higher than the estimated error of chosen ARIMA. So, according to this study, it can be said that the ARIMA model is better than ANN model for forecasting the rice production in Bangladesh. Dhaka Univ. J. Sci. 68(2): 143-147, 2020 (July)

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