PDF下載
[1]彭森,程蕊,吳卿,等.基于極限學(xué)習(xí)機(jī)算法的供水管網(wǎng)爆管識(shí)別研究[J].中國(guó)給水排水,2022,38(7):56-62.
PENGSen,CHENGRui,WUQing,et al.Identification of Water Supply Network Pipe Burst Based on Extreme Learning Machine Algorithm[J].China Water & Wastewater,2022,38(7):56-62.點(diǎn)擊復(fù)制
基于極限學(xué)習(xí)機(jī)算法的供水管網(wǎng)爆管識(shí)別研究
中國(guó)給水排水[ISSN:1000-4062/CN:12-1073/TU] 卷: 第38卷 期數(shù): 2022年第7期 頁(yè)碼: 56-62 欄目: 出版日期: 2022-04-01
Title:Identification of Water Supply Network Pipe Burst Based on Extreme Learning Machine Algorithm
作者:彭森1， 程蕊1， 吳卿1， 程景1， 孟濤2
Author(s):PENG Sen1， CHENG Rui1， WU Qing1， CHENG Jing1， MENG Tao2（1. School of Environmental Science and Engineering， Tianjin University， Tianjin 300350， China； 2. North China Municipal Engineering Design & Research Institute Co. Ltd.， Tianjin 300381， China）
關(guān)鍵詞:（1.天津大學(xué) 環(huán)境科學(xué)與工程學(xué)院; 天津 300350; 2.中國(guó)市政工程華北設(shè)計(jì)研究總院有限公司; 天津 300381）
Keywords:water supply network; identification of pipe burst area; ELM algorithm; K-means clustering algorithm
摘要:供水管網(wǎng)爆管具有定位難、影響范圍廣的特點(diǎn)，長(zhǎng)期困擾著供水企業(yè)。針對(duì)供水管網(wǎng)爆管區(qū)域識(shí)別問(wèn)題，綜合考慮多種影響因素下的爆管工況，利用爆管特征值矩陣構(gòu)建爆管樣本數(shù)據(jù)集，采用極限學(xué)習(xí)機(jī)算法（ELM）建立爆管區(qū)域識(shí)別模型；應(yīng)用K-means聚類算法分析節(jié)點(diǎn)水力變化特征的相似性，并在此基礎(chǔ)上對(duì)管網(wǎng)進(jìn)行監(jiān)測(cè)區(qū)域劃分與監(jiān)測(cè)點(diǎn)布設(shè)，形成多種監(jiān)測(cè)方案；綜合爆管識(shí)別率等參數(shù)，分析ELM在不同監(jiān)測(cè)方案以及在噪聲影響下的識(shí)別性能。采用實(shí)際管網(wǎng)算例進(jìn)行了爆管區(qū)域識(shí)別分析，結(jié)果表明：該模型可以進(jìn)行有效的爆管區(qū)域識(shí)別，同時(shí)結(jié)合不同分區(qū)方案可以提高爆管識(shí)別率；監(jiān)測(cè)點(diǎn)的增加可以減小壓力監(jiān)測(cè)數(shù)據(jù)的噪聲影響。
Abstract:Water supply network pipe burst has the characteristics of difficult location and wide range of influence, which has troubled water supply enterprises for a long time. To solve the problem of identifying the pipe burst area of water supply networks, the burst conditions were comprehensively considered under various influencing factors, the pipe burst sample data set was constructed by using eigenvalue matrix, and the model for identifying the pipe burst area was established by extreme learning machine (ELM) algorithm. The similarity of node hydraulic change characteristics was analyzed by using K-means clustering algorithm. On this basis, the monitoring area of the pipe network was divided and the monitoring points were arranged to form a variety of monitoring schemes. The identification performance of ELM under different monitoring schemes and noise impact was analyzed by combining the identification rate of pipe burst and other parameters. The burst area was identified and analyzed in a practical pipe network. It was found that the model could effectively identify the pipe burst area. At the same time，it could effectively improve the identification rate of pipe burst by combining different zoning schemes. The addition of monitoring points could reduce the noise impact from pressure monitoring data.
更新日期/Last Update: 2022-04-01
[1]彭森,程蕊,吳卿,等.基于極限學(xué)習(xí)機(jī)算法的供水管網(wǎng)爆管識(shí)別研究[J].中國(guó)給水排水,2022,38(7):56-62.
PENGSen,CHENGRui,WUQing,et al.Identification of Water Supply Network Pipe Burst Based on Extreme Learning Machine Algorithm[J].China Water & Wastewater,2022,38(7):56-62.
點(diǎn)擊復(fù)制
PENGSen,CHENGRui,WUQing,et al.Identification of Water Supply Network Pipe Burst Based on Extreme Learning Machine Algorithm[J].China Water & Wastewater,2022,38(7):56-62.
基于極限學(xué)習(xí)機(jī)算法的供水管網(wǎng)爆管識(shí)別研究
中國(guó)給水排水[ISSN:1000-4062/CN:12-1073/TU] 卷: 第38卷 期數(shù): 2022年第7期 頁(yè)碼: 56-62 欄目: 出版日期: 2022-04-01
- Title:
- Identification of Water Supply Network Pipe Burst Based on Extreme Learning Machine Algorithm
- 關(guān)鍵詞:
- （1.天津大學(xué) 環(huán)境科學(xué)與工程學(xué)院; 天津 300350; 2.中國(guó)市政工程華北設(shè)計(jì)研究總院有限公司; 天津 300381）
- Keywords:
- water supply network; identification of pipe burst area; ELM algorithm; K-means clustering algorithm
- 摘要:
- 供水管網(wǎng)爆管具有定位難、影響范圍廣的特點(diǎn)，長(zhǎng)期困擾著供水企業(yè)。針對(duì)供水管網(wǎng)爆管區(qū)域識(shí)別問(wèn)題，綜合考慮多種影響因素下的爆管工況，利用爆管特征值矩陣構(gòu)建爆管樣本數(shù)據(jù)集，采用極限學(xué)習(xí)機(jī)算法（ELM）建立爆管區(qū)域識(shí)別模型；應(yīng)用K-means聚類算法分析節(jié)點(diǎn)水力變化特征的相似性，并在此基礎(chǔ)上對(duì)管網(wǎng)進(jìn)行監(jiān)測(cè)區(qū)域劃分與監(jiān)測(cè)點(diǎn)布設(shè)，形成多種監(jiān)測(cè)方案；綜合爆管識(shí)別率等參數(shù)，分析ELM在不同監(jiān)測(cè)方案以及在噪聲影響下的識(shí)別性能。采用實(shí)際管網(wǎng)算例進(jìn)行了爆管區(qū)域識(shí)別分析，結(jié)果表明：該模型可以進(jìn)行有效的爆管區(qū)域識(shí)別，同時(shí)結(jié)合不同分區(qū)方案可以提高爆管識(shí)別率；監(jiān)測(cè)點(diǎn)的增加可以減小壓力監(jiān)測(cè)數(shù)據(jù)的噪聲影響。
- Abstract:
- Water supply network pipe burst has the characteristics of difficult location and wide range of influence, which has troubled water supply enterprises for a long time. To solve the problem of identifying the pipe burst area of water supply networks, the burst conditions were comprehensively considered under various influencing factors, the pipe burst sample data set was constructed by using eigenvalue matrix, and the model for identifying the pipe burst area was established by extreme learning machine (ELM) algorithm. The similarity of node hydraulic change characteristics was analyzed by using K-means clustering algorithm. On this basis, the monitoring area of the pipe network was divided and the monitoring points were arranged to form a variety of monitoring schemes. The identification performance of ELM under different monitoring schemes and noise impact was analyzed by combining the identification rate of pipe burst and other parameters. The burst area was identified and analyzed in a practical pipe network. It was found that the model could effectively identify the pipe burst area. At the same time，it could effectively improve the identification rate of pipe burst by combining different zoning schemes. The addition of monitoring points could reduce the noise impact from pressure monitoring data.
更新日期/Last Update: 2022-04-01
