Improved PSO in Water Supply Systems Based on AHP-RS and RBF Neural Network

2011 ◽  
Vol 99-100 ◽  
pp. 199-202
Author(s):  
Ao Jie Wang ◽  
Chao Lue Liu
Keyword(s):  
Neural Network ◽  
Distribution Systems ◽  
Water Distribution ◽  
Rough Set Theory ◽  
Rbf Neural Network ◽  
Evaluation Model ◽  
Basic Function ◽  
Attractive Alternative ◽  
Water Supply Systems ◽  
Improved Pso

A evaluation model based on the integration of analytic hierarchy process(AHP)-rough set theory (RS) and radial basic function (RBF) neural network is put forward for grasping the hydropower project financing risk.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 AHP-RS and RBF neural network connecting with improved PSO method is an attractive alternative to the conventional regression analysis method in modeling water distribution systems.

The Evaluation Model of the Hydropower Project Financing Risk Based on AHP-RS and RBF Neural Network

2011 ◽  
Vol 474-476 ◽  
pp. 2243-2246 ◽  
Author(s):  
Hui Zhao ◽  
Li Ming Chen
Keyword(s):  
Neural Network ◽  
Rough Set Theory ◽  
Rbf Neural Network ◽  
Evaluation Model ◽  
Indicator System ◽  
Basic Function ◽  
Hydropower Project ◽  
Project Financing ◽  
Analytic Hierarchy ◽  
Financing Risk

A evaluation model based on the integration of analytic hierarchy process (AHP)-rough set theory (RS) and radial basic function (RBF) neural network is put forward for grasping the hydropower project financing risk. Firstly, the evaluation indicator system is constructed by AHP, then the evaluation indicators are discretized by RS neural network. And then, RBF neural network is used to evaluate the hydropower project financing risk. In order to grasp this evaluation model better, finally, the paper provides an example to demonstrate the application of this evaluation model.

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 Graph Neural Network for Integrated Water Network Partitioning and Dynamic District Metered Areas

2021 ◽  
Author(s):  
KEZHEN RONG ◽  
Minglei Fu ◽  
JIAWEI CHEN ◽  
LEJIN ZHENG ◽  
JIANFENG ZHENG ◽  
...  
Keyword(s):  
Neural Network ◽  
Distribution Systems ◽  
Water Distribution ◽  
Distribution Networks ◽  
Pollution Monitoring ◽  
Practical Case ◽  
Network Partitioning ◽  
Water Network ◽  
Search Range ◽  
Water Network Partitioning

Abstract Water distribution systems (WDSs) are used to transmit and distribute water resources in cities. Water distribution networks (WDNs) are partitioned into district metered areas (DMAs) by water network partitioning (WNP), which can be used for leak control, pollution monitoring, and pressure optimization in WDS management. In order to overcome the limitations of optimal search range and the decrease of recovery ability caused by two-step WNP and fixed DMAs in previous studies, this study developed a new method combining a graph neural network to realize integrated WNP and dynamic DMAs to optimize WDS management and respond to emergencies. The proposed method was tested in a practical case study; the results showed that good hydraulic performance of the WDN was maintained and that dynamic DMAs demonstrated excellent stability in emergency situations, which proves the effectiveness of the method in WNP.

scholarly journals A Novel Evaluation Model for Urban Smart Growth Based on Principal Component Regression and Radial Basis Function Neural Network

2019 ◽  
Vol 11 (21) ◽  
pp. 6125
Author(s):  
Lianyan Li ◽  
Xiaobin Ren
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rbf Neural Network ◽  
Evaluation Model ◽  
Smart Growth ◽  
Principal Component Regression ◽  
Principal Component ◽  
Self Organizing Map ◽  
Radial Basis

Smart growth is widely adopted by urban planners as an innovative approach, which can guide a city to develop into an environmentally friendly modern city. Therefore, determining the degree of smart growth is quite significant. In this paper, sustainable degree (SD) is proposed to evaluate the level of urban smart growth, which is established by principal component regression (PCR) and the radial basis function (RBF) neural network. In the case study of Yumen and Otago, the SD values of Yumen and Otago are 0.04482 and 0.04591, respectively, and both plans are moderately successful. Yumen should give more attention to environmental development while Otago should concentrate on economic development. In order to make a reliable future plan, a self-organizing map (SOM) is conducted to classify all indicators and the RBF neural network-trained indicators are separate under different classifications to output new plans. Finally, the reliability of the plan is confirmed by cellular automata (CA). Through simulation of the trend of urban development, it is found that the development speed of Yumen and Otago would increase slowly in the long term. This paper provides a powerful reference for cities pursuing smart growth.

scholarly journals Trihalomethanes formation in Iranian water supply systems: predicting and modeling

2015 ◽  
Vol 13 (3) ◽  
pp. 859-869 ◽  
Author(s):  
Ali Akbar Babaei ◽  
Leila Atari ◽  
Mehdi Ahmadi ◽  
Kambiz Ahmadiangali ◽  
Mirzaman Zamanzadeh ◽  
...  
Keyword(s):  
Drinking Water ◽  
Water Temperature ◽  
Distribution Systems ◽  
Water Distribution ◽  
Pearson Correlation ◽  
Multiple Linear Regression Model ◽  
Residual Chlorine ◽  
Water Supply Systems ◽  
Drinking Water Distribution Systems ◽  
Bromide Ion

Trihalomethanes (THMs) were the first disinfection by-products discovered in drinking water and are classified as probable carcinogens. This study measures and models THMs formation at two drinking water distribution systems (WDS1 and WDS2) in Ahvaz City, Iran. The investigation was based on field-scale investigations and an intensive 36-week sampling program, from January to September 2011. The results showed total THM concentrations in the range 17.4–174.8 μg/L and 18.9–99.5 μg/L in WDS1 and WDS2, respectively. Except in a few cases, the THM concentrations in WDS1 and WDS2 were lower than the maximum contaminant level values. Using two-tailed Pearson correlation test, the water temperature, dissolved organic carbon, UV254, bromide ion (Br−), free residual chlorine, and chlorine dose were identified as the significant parameters for THMs formation in WDS2. Water temperature was the only significant parameter for THMs formation in WDS1. Based on the correlation results, a predictive model for THMs formation was developed using a multiple regression approach. A multiple linear regression model showed the best fit according to the coefficients of determination (R2) obtained for WDS1 (R2 = 0.47) and WDS2 (R2 = 0.54). Further correlation studies and analysis focusing on THMs formation are necessary to assess THMs concentration using the predictive models.

scholarly journals Burst Detection in Water Distribution Systems: The Issue of Dataset Collection

2020 ◽  
Vol 10 (22) ◽  
pp. 8219
Author(s):  
Andrea Menapace ◽  
Ariele Zanfei ◽  
Manuel Felicetti ◽  
Diego Avesani ◽  
Maurizio Righetti ◽  
...  
Keyword(s):  
Water Supply ◽  
Water Consumption ◽  
Distribution System ◽  
Smart Grids ◽  
Distribution Systems ◽  
Water Distribution ◽  
Stochastic Modelling ◽  
Water Supply Systems ◽  
Consumption Data ◽  
Supply Systems

Developing data-driven models for bursts detection is currently a demanding challenge for efficient and sustainable management of water supply systems. The main limit in the progress of these models lies in the large amount of accurate data required. The aim is to present a methodology for the generation of reliable data, which are fundamental to train anomaly detection models and set alarms. Thus, the results of the proposed methodology is to provide suitable water consumption data. The presented procedure consists of stochastic modelling of water request and hydraulic pipes bursts simulation to yield suitable synthetic time series of flow rates, for instance, inlet flows of district metered areas and small water supply systems. The water request is obtained through the superimposition of different components, such as the daily, the weekly, and the yearly trends jointly with a random normal distributed component based on the consumption mean and variance, and the number of users aggregation. The resulting request is implemented into the hydraulic model of the distribution system, also embedding background leaks and bursts using a pressure-driven approach with both concentrated and distributed demand schemes. This work seeks to close the gap in the field of synthetic generation of drinking water consumption data, by establishing a proper dedicated methodology that aims to support future water smart grids.

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