Prediction of Damage∕Repair Rates in Water Distribution Systems Due to Seismic Events Using Artificial Neural Network

2007 ◽  
pp. 149-154 ◽  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution Systems ◽  
Seismic Events ◽  
Damage Repair ◽  
Artificial Neural

Application of Artificial Neural Network Model Based on Improved PSO in Water Supply Systems

2011 ◽  
Vol 267 ◽  
pp. 609-613
Author(s):  
Hong Xiang Wang ◽  
Wen Xian Guo
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Distribution Systems ◽  
Water Distribution ◽  
Microscopic Model ◽  
Macroscopic Model ◽  
Control Element ◽  
Calibration Data ◽  
Artificial Neural ◽  
Improved Pso

Parameter calibration, data collection and simulation to control element were used to improve the accuracy of microscopic model. In order to overcome the shortage of macroscopic model, theoretical and empirical equation was adopted. The artificial neural network based on PSO method was introduced to improve simulation ability of water distribution system model from microscopic model and macroscopic model. There are two hidden layers with a maximum of 64 nodes per layer in the model. The Particle Swarm Optimization (PSO) algorithm is implemented to optimize the node numbers of the hidden layers in the model. The study indicates that the artificial neural network connecting with improved PSO method is an attractive alternative to the conventional regression analysis method in modeling water distribution systems.

scholarly journals Development of a Cyberattack Detection Model for a Water Distribution System using Water Quality and Hydraulic Criteria

2020 ◽  
Vol 20 (5) ◽  
pp. 47-56
Author(s):  
Kyoung Won Min ◽  
Young Hwan Choi ◽  
Joong Hoon Kim
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Distribution Systems ◽  
Water Distribution ◽  
Detection Performance ◽  
Economic Losses ◽  
Water Quality Criteria ◽  
Quality Factors ◽  
Artificial Neural

In recent years, Cyber-Physical Systems (CPSs) have been applied to Water Distribution Systems (WDSs) to facilitate efficient operation and maintenance. Since data are transmitted through the network in such systems, a cyberattack can disrupt the operation of WDSs, for example, by causing water supply reduction, water pollution, and economic losses. In the past few years, cyberattack detection algorithms and various cyberattack scenarios have been proposed. These studies considered either hydraulic factors, such as pipe velocity, nodal pressure, or tank level, or water quality factors. However, an algorithm which considers only one factor cannot prevent the various problems that may arise, such as water quality issues, and the hydraulic and quality factors have a correlation. Therefore, in this study, a framework was developed by considering both hydraulic and water quality factors. The proposed approach was applied to an artificial neural network model. Performance indicators were used to examine the detection performance according to the parameters of the artificial neural network. By comparing the detection performance when only hydraulic factors were considered and the performance when both hydraulic and water quality factors were considered, the effectiveness of the algorithm that consider both hydraulic and water quality factors was demonstrated. A cyberattack detection algorithm that considers both hydraulic and water quality criteria can be applicable in more realistic scenarios and contribute to the establishment of safe infrastructure for the entire process of designing and operating WDSs with CPSs.

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