基于融合注意力机制LSTM网络的地下水位自适应鲁棒预测
TV211.12; 地下水水位是旱天污水管网地下水入渗量的重要影响因素,快速精准地预测地下水水位能有效提升旱天污水管网地下水入渗量估算准确度,辅助优化管网病害治理与维护策略.针对目前城市复杂水文预测存在的准确度低、灵敏度低、泛化能力弱等问题,本文提出了一种新的鲁棒自适应水位预测算法.首先,对水文数据进行预处理,解决了数据时间跨度大、噪声多、缺失及异常、非平稳等问题.其次,针对不同输入特征对预测指标的影响,在模型训练阶段提出一种新的空间变量注意机制,可快速识别与水位关联的关键变量,并对输入特征赋予不同的影响权重.然后,针对不同序列长度对预测效果的影响,还设计了自适应时间注意力机制,帮助网络自适应...
Saved in:
| Published in | 工程科学与技术 Vol. 56; no. 1; pp. 54 - 64 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | Chinese |
| Published |
四川大学 电气工程学院,四川 成都 610065%成都兴蓉市政设施管理有限公司,四川 成都 610065
2024
|
| Subjects | |
| Online Access | Get full text |
| ISSN | 2096-3246 |
| DOI | 10.15961/j.jsuese.202300315 |
Cover
| Abstract | TV211.12; 地下水水位是旱天污水管网地下水入渗量的重要影响因素,快速精准地预测地下水水位能有效提升旱天污水管网地下水入渗量估算准确度,辅助优化管网病害治理与维护策略.针对目前城市复杂水文预测存在的准确度低、灵敏度低、泛化能力弱等问题,本文提出了一种新的鲁棒自适应水位预测算法.首先,对水文数据进行预处理,解决了数据时间跨度大、噪声多、缺失及异常、非平稳等问题.其次,针对不同输入特征对预测指标的影响,在模型训练阶段提出一种新的空间变量注意机制,可快速识别与水位关联的关键变量,并对输入特征赋予不同的影响权重.然后,针对不同序列长度对预测效果的影响,还设计了自适应时间注意力机制,帮助网络自适应地找出与不同时间序列长度预测指标相关的编码器隐藏状态,以更好地捕捉时间上的依赖关系.在此基础上,以上下文向量作为输入,提出一种融合注意力机制的长短时记忆网络水文预测算法.最后,通过意大利Petrignano水文数据验证了所提算法的有效性,并与GRU、Elman、LSTM、VA-LSTM和S-LSTM等方法进行预测性能比较.结果表明,基于融合注意力机制的LSTM网络在面临大规模、噪点多的复杂数据时有优于其它几种算法的预测效果,表明该算法具有强自适应性和鲁棒性.本文研究结果可以为市政排水策略合理调整、及时控制提供参考. |
|---|---|
| AbstractList | TV211.12; 地下水水位是旱天污水管网地下水入渗量的重要影响因素,快速精准地预测地下水水位能有效提升旱天污水管网地下水入渗量估算准确度,辅助优化管网病害治理与维护策略.针对目前城市复杂水文预测存在的准确度低、灵敏度低、泛化能力弱等问题,本文提出了一种新的鲁棒自适应水位预测算法.首先,对水文数据进行预处理,解决了数据时间跨度大、噪声多、缺失及异常、非平稳等问题.其次,针对不同输入特征对预测指标的影响,在模型训练阶段提出一种新的空间变量注意机制,可快速识别与水位关联的关键变量,并对输入特征赋予不同的影响权重.然后,针对不同序列长度对预测效果的影响,还设计了自适应时间注意力机制,帮助网络自适应地找出与不同时间序列长度预测指标相关的编码器隐藏状态,以更好地捕捉时间上的依赖关系.在此基础上,以上下文向量作为输入,提出一种融合注意力机制的长短时记忆网络水文预测算法.最后,通过意大利Petrignano水文数据验证了所提算法的有效性,并与GRU、Elman、LSTM、VA-LSTM和S-LSTM等方法进行预测性能比较.结果表明,基于融合注意力机制的LSTM网络在面临大规模、噪点多的复杂数据时有优于其它几种算法的预测效果,表明该算法具有强自适应性和鲁棒性.本文研究结果可以为市政排水策略合理调整、及时控制提供参考. |
| Abstract_FL | Groundwater level is an important factor affecting groundwater infiltration of sewage pipe network in dry weather.Accurate prediction of groundwater level can effectively improve the accuracy of groundwater infiltration estimation in dry weather,and assist in optimizing pipe net-work disease control and maintenance strategies.Aiming at the problems of low accuracy,low sensitivity,and weak generalization ability in the current urban complex hydrological prediction,a new robust adaptive water level prediction algorithm was proposed in this paper.First,a prior processing was carried out on the hydrological data,which solved the problems of large time span,high noise,missing and abnormal,and non-sta-tionary data.Secondly,in view of the influence difference of input features on predictive indicators,a new spatial variable attention mechanism was proposed in model training stage,which can quickly identify key variables associated with water levels and assign different influence weights to input features.Furthermore,in view of the influence difference of various sequence lengths on the prediction effect,an adaptive temporal atten-tion mechanism was also designed to adaptively find out the hidden state of the encoder related to the predictors of different sequence lengths,so as to capture time dependencies.On this basis,with the context vector as the input,an LSTM hydrological prediction algorithm integrating atten-tion mechanism was proposed.Finally,the effectiveness of the proposed algorithm was verified by the hydrological data of Petrignano,Italy.The prediction performance was compared with GRU,Elman,LSTM,VA-LSTM and S-LSTM methods.The results showed that the proposed STA-LSTM network based on the fusion attention mechanism has a better prediction effect than other algorithms when faced with complex,large-scale,and noisy data,indicating the strong adaptability and robustness of the algorithm.The research results of the paper provide a reference for the reasonable adjustment and timely control of municipal drainage strategies. |
| Author | 厉潇滢 杨丹 郭斌 佃松宜 芮胜阳 |
| AuthorAffiliation | 四川大学 电气工程学院,四川 成都 610065%成都兴蓉市政设施管理有限公司,四川 成都 610065 |
| AuthorAffiliation_xml | – name: 四川大学 电气工程学院,四川 成都 610065%成都兴蓉市政设施管理有限公司,四川 成都 610065 |
| Author_FL | RUI Shengyang YANG Dan DIAN Songyi GUO Bin LI Xiaoying |
| Author_FL_xml | – sequence: 1 fullname: DIAN Songyi – sequence: 2 fullname: LI Xiaoying – sequence: 3 fullname: YANG Dan – sequence: 4 fullname: RUI Shengyang – sequence: 5 fullname: GUO Bin |
| Author_xml | – sequence: 1 fullname: 佃松宜 – sequence: 2 fullname: 厉潇滢 – sequence: 3 fullname: 杨丹 – sequence: 4 fullname: 芮胜阳 – sequence: 5 fullname: 郭斌 |
| BookMark | eNotj8tKw0AUQGdRwVr7Bf6CiXdmMpNkKcVHIeLCug6TSVKskoJDMUuVgMVSX7jSoKAbcVV8IZH-TZO2f2FBV2d3DmcBlaJ2FCC0hEHHzOZ4paW3VCdQgU6AUACKWQmVCdhco8Tg86iq1J4HlBuUMc7KqJ4_ZqPsYvLQz6-6xftLkVzm5_dFmuXdL2ensTUeXo9_0vFdkqeD0XevGHyMhv3J2ev0-DTPbqdvJ8XzzfQpKT57i2guFAcqqP6zgnbX1xq1Tc3Z3qjXVh1NYTC5hsEQzJLU8z3GRUg9Q1jYtyUDQzKTCWJJEESaNPTADn1qcV8EpiAYQhOY6dMKWv7zHokoFFHTbbU7h9Gs6Crpx7HnNuV-PLs3AANw-gu3omvw |
| ClassificationCodes | TV211.12 |
| ContentType | Journal Article |
| Copyright | Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
| Copyright_xml | – notice: Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
| DBID | 2B. 4A8 92I 93N PSX TCJ |
| DOI | 10.15961/j.jsuese.202300315 |
| DatabaseName | Wanfang Data Journals - Hong Kong WANFANG Data Centre Wanfang Data Journals 万方数据期刊 - 香港版 China Online Journals (COJ) China Online Journals (COJ) |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| DocumentTitle_FL | Adaptive Robust Prediction of Groundwater Level Based on Fusion Attention Mechanism LSTM Network |
| EndPage | 64 |
| ExternalDocumentID | scdxxb_gckx202401006 |
| GrantInformation_xml | – fundername: 国家重点研发计划 funderid: (2020YFB1709705) |
| GroupedDBID | -0C -SC -S~ 2B. 2RA 4A8 5VR 92I 92M 93N 9D9 9DC AFUIB ALMA_UNASSIGNED_HOLDINGS CAJEC CQIGP GROUPED_DOAJ PB1 PB9 PSX Q-- R-C RT3 T8S TCJ U1F U5C |
| ID | FETCH-LOGICAL-s1076-104a58c3bdb56af3b4a81d9c504c575a28c0a2c73fb09fd386dae7a210f7057d3 |
| ISSN | 2096-3246 |
| IngestDate | Thu May 29 03:53:57 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | LSTM网络 LSTM spatial-temporal attention mechanism 自适应预测 robust prediction 鲁棒预测 时间与空间注意力机制 groundwater level prediction 地下水位预测 adaptive prediction |
| Language | Chinese |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-s1076-104a58c3bdb56af3b4a81d9c504c575a28c0a2c73fb09fd386dae7a210f7057d3 |
| PageCount | 11 |
| ParticipantIDs | wanfang_journals_scdxxb_gckx202401006 |
| PublicationCentury | 2000 |
| PublicationDate | 2024 |
| PublicationDateYYYYMMDD | 2024-01-01 |
| PublicationDate_xml | – year: 2024 text: 2024 |
| PublicationDecade | 2020 |
| PublicationTitle | 工程科学与技术 |
| PublicationTitle_FL | Advanced Engineering Sciences |
| PublicationYear | 2024 |
| Publisher | 四川大学 电气工程学院,四川 成都 610065%成都兴蓉市政设施管理有限公司,四川 成都 610065 |
| Publisher_xml | – name: 四川大学 电气工程学院,四川 成都 610065%成都兴蓉市政设施管理有限公司,四川 成都 610065 |
| SSID | ssib036435565 ssib050593459 ssib041261190 ssib030194745 ssib051371919 ssj0003313526 ssib027967859 |
| Score | 2.384715 |
| Snippet | TV211.12; 地下水水位是旱天污水管网地下水入渗量的重要影响因素,快速精准地预测地下水水位能有效提升旱天污水管网地下水入渗量估算准确度,辅助优化管网病害治理与维护策... |
| SourceID | wanfang |
| SourceType | Aggregation Database |
| StartPage | 54 |
| Title | 基于融合注意力机制LSTM网络的地下水位自适应鲁棒预测 |
| URI | https://d.wanfangdata.com.cn/periodical/scdxxb-gckx202401006 |
| Volume | 56 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals issn: 2096-3246 databaseCode: DOA dateStart: 20220101 customDbUrl: isFulltext: true dateEnd: 99991231 titleUrlDefault: https://www.doaj.org/ omitProxy: true ssIdentifier: ssj0003313526 providerName: Directory of Open Access Journals |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3Na9RAFA-lXryIouI3PTinsprMZJKZ46SbpYr1Ygu9SJkkm4rCCraF0pNKwWKpX3jSoqAX8VT8Qlb633S37X_hey_Zzcr20HoJs_PxPn5vyLw3O3njOFczP7Vpzr0ab2oFAYqVNQvLcs3NcpsEYS6Fxo-Tp24HkzP-zVk5OzJ6d-DU0tJici1dOfC7kv-xKtSBXfEr2SNYtk8UKqAM9oUnWBieh7IxiyXTDRYZFvv4VDGLFdMxU3VqcplSLA5YJJihggLPsYFNyjAdYY2eoOESe0bBrTvTUywOWVRn2qNChD2goA0ORqIwwCV-iikiAT8jn2rqxBjqQ2aAqMZjFIrjKOChfayJOPiuOMoIpjnWGE6UgQ4IEQ06yzQQSEkUwBTsoBCSbJKZOjNBT5KY1DPIsdDK9I8s90QT1FJnUUyjY-hVdZFIQmmSo44KYCEC4aouNBhxJI6RrloUcjYEPnBBwDTT0EUM7qnwajeVcIzIAqShJnMZH3XrKTZOqPuISoGx9g_Cow8DcCQs-cQw9XECR-GEQKMIUHEcHFrwCxmXw22EhiSjQr0gWCShzAkFEKqBTaBxRLhrAg2FipmheYNTLygtgcNJOmRBlA3JCDMRaB5N3mrd4hAV18BPDwYX2SJ7_D8vk2LFLFKIl75XkdB-aFWXOiiW9ft4ISUmt4XAGa8nqZyY_tHShTRbXk7m5tMHy2hZ16OE_Mc4btcN7LjAcsNDDZ5a5c3DUqf9gcsfBDjrUlaHAHyPB55X_aks8TbMgVRM0hOhp8vsiOgICuHh7RN4vWUPkjLvGSp0fVgd-nCwldvW_ICPO33SOVEGp2OmeNOcckZW7p12bnQ-tnfaL_Y-bHRerXW_f-muvuw8f9_dbHfWfuH7Ynf79e6fzd13q53NrZ3f692tHzvbG3vPvu4_ftppv93_9qT7-c3-p9Xuz_Uzzkwjnp6YrJUXsNQWPDcMwEXzrVSpSLJEBjYXiW8hvNWpdP0UwjzLVepanoYiT1ydZ0IFmW2GlntuHkIcmImzzmjrYat5zhlTmYJILg3BH7aYSMGqMM8sFxk4DU2h8_MOK1WfK1-wC3MHmfLCIftddI5judgsveSMLj5aal6G8GExuUKT4C8NBM_c |
| linkProvider | Directory of Open Access Journals |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=%E5%9F%BA%E4%BA%8E%E8%9E%8D%E5%90%88%E6%B3%A8%E6%84%8F%E5%8A%9B%E6%9C%BA%E5%88%B6LSTM%E7%BD%91%E7%BB%9C%E7%9A%84%E5%9C%B0%E4%B8%8B%E6%B0%B4%E4%BD%8D%E8%87%AA%E9%80%82%E5%BA%94%E9%B2%81%E6%A3%92%E9%A2%84%E6%B5%8B&rft.jtitle=%E5%B7%A5%E7%A8%8B%E7%A7%91%E5%AD%A6%E4%B8%8E%E6%8A%80%E6%9C%AF&rft.au=%E4%BD%83%E6%9D%BE%E5%AE%9C&rft.au=%E5%8E%89%E6%BD%87%E6%BB%A2&rft.au=%E6%9D%A8%E4%B8%B9&rft.au=%E8%8A%AE%E8%83%9C%E9%98%B3&rft.date=2024&rft.pub=%E5%9B%9B%E5%B7%9D%E5%A4%A7%E5%AD%A6+%E7%94%B5%E6%B0%94%E5%B7%A5%E7%A8%8B%E5%AD%A6%E9%99%A2%2C%E5%9B%9B%E5%B7%9D+%E6%88%90%E9%83%BD+610065%25%E6%88%90%E9%83%BD%E5%85%B4%E8%93%89%E5%B8%82%E6%94%BF%E8%AE%BE%E6%96%BD%E7%AE%A1%E7%90%86%E6%9C%89%E9%99%90%E5%85%AC%E5%8F%B8%2C%E5%9B%9B%E5%B7%9D+%E6%88%90%E9%83%BD+610065&rft.issn=2096-3246&rft.volume=56&rft.issue=1&rft.spage=54&rft.epage=64&rft_id=info:doi/10.15961%2Fj.jsuese.202300315&rft.externalDocID=scdxxb_gckx202401006 |
| thumbnail_s | http://utb.summon.serialssolutions.com/2.0.0/image/custom?url=http%3A%2F%2Fwww.wanfangdata.com.cn%2Fimages%2FPeriodicalImages%2Fscdxxb-gckx%2Fscdxxb-gckx.jpg |