{"id":"https://openalex.org/W3095364083","doi":"https://doi.org/10.1109/lgrs.2020.3030839","title":"Hybrid Attention Networks for Flow and Pressure Forecasting in Water Distribution Systems","display_name":"Hybrid Attention Networks for Flow and Pressure Forecasting in Water Distribution Systems","publication_year":2020,"publication_date":"2020-10-27","ids":{"openalex":"https://openalex.org/W3095364083","doi":"https://doi.org/10.1109/lgrs.2020.3030839","mag":"3095364083"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2020.3030839","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2020.3030839","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Geoscience and Remote Sensing Letters","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5035357260","display_name":"Ziqing Ma","orcid":"https://orcid.org/0000-0003-1567-5054"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ziqing Ma","raw_affiliation_strings":["School of Environment, Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-1567-5054","affiliations":[{"raw_affiliation_string":"School of Environment, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100664295","display_name":"Shuming Liu","orcid":"https://orcid.org/0000-0002-4949-4318"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuming Liu","raw_affiliation_strings":["School of Environment, Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-4949-4318","affiliations":[{"raw_affiliation_string":"School of Environment, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044651257","display_name":"Guancheng Guo","orcid":"https://orcid.org/0000-0002-0396-5524"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guancheng Guo","raw_affiliation_strings":["School of Environment, Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-0396-5524","affiliations":[{"raw_affiliation_string":"School of Environment, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102186053","display_name":"Xipeng Yu","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xipeng Yu","raw_affiliation_strings":["School of Environment, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Environment, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":0.5735,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.6625161,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"19","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11220","display_name":"Water Systems and Optimization","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11220","display_name":"Water Systems and Optimization","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11309","display_name":"Music and Audio Processing","score":0.9966999888420105,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9962000250816345,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.753754734992981},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.5263568162918091},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4905833303928375},{"id":"https://openalex.org/keywords/dual","display_name":"Dual (grammatical number)","score":0.4867532253265381},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.45792895555496216},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.4567916691303253},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4545414447784424},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.4337366819381714},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4215964674949646},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3407195210456848}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.753754734992981},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.5263568162918091},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4905833303928375},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.4867532253265381},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.45792895555496216},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.4567916691303253},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4545414447784424},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4337366819381714},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4215964674949646},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3407195210456848},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C124952713","wikidata":"https://www.wikidata.org/wiki/Q8242","display_name":"Literature","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lgrs.2020.3030839","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2020.3030839","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Geoscience and Remote Sensing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Clean water and sanitation","score":0.6100000143051147,"id":"https://metadata.un.org/sdg/6"}],"awards":[{"id":"https://openalex.org/G4789985809","display_name":null,"funder_award_id":"51879139","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W2062981820","https://openalex.org/W2064675550","https://openalex.org/W2130942839","https://openalex.org/W2133564696","https://openalex.org/W2141692439","https://openalex.org/W2146268548","https://openalex.org/W2195290284","https://openalex.org/W2553205900","https://openalex.org/W2613328025","https://openalex.org/W2808535700","https://openalex.org/W2893564989","https://openalex.org/W2937537592","https://openalex.org/W2963064196","https://openalex.org/W3004916592","https://openalex.org/W4385245566","https://openalex.org/W6679434410","https://openalex.org/W6679436768","https://openalex.org/W6739901393","https://openalex.org/W6761699050"],"related_works":["https://openalex.org/W4390516098","https://openalex.org/W2406638334","https://openalex.org/W2181948922","https://openalex.org/W2384362569","https://openalex.org/W2142795561","https://openalex.org/W1991765889","https://openalex.org/W1990068454","https://openalex.org/W2472172556","https://openalex.org/W4399531511","https://openalex.org/W4385335406"],"abstract_inverted_index":{"Multivariate":[0],"geo-sensory":[1],"time":[2,39],"series":[3,40],"prediction":[4],"is":[5,98],"challenging":[6],"because":[7],"of":[8,33,42,71],"the":[9,34,93],"complex":[10],"spatial":[11,75,86,96],"and":[12,37,50,78,95,119],"temporal":[13,80,94],"correlations.":[14],"In":[15],"urban":[16],"water":[17],"distribution":[18],"systems":[19],"(WDSs),":[20],"numerous":[21],"spatial-correlated":[22],"sensors":[23],"have":[24],"been":[25],"deployed":[26],"to":[27],"continuously":[28],"collect":[29],"hydraulic":[30],"data.":[31],"Forecasts":[32],"monitored":[35],"flow":[36,118],"pressure":[38,120],"are":[41,106],"vital":[43],"importance":[44],"for":[45],"operational":[46],"decision":[47],"making,":[48],"alerts,":[49],"anomaly":[51],"detection.":[52],"To":[53],"address":[54],"this":[55],"issue,":[56],"we":[57],"proposed":[58],"a":[59,74,79,84,102],"hybrid":[60,85],"dual-stage":[61],"spatial\u2013temporal":[62],"attention-based":[63,76,81],"recurrent":[64],"neural":[65],"networks":[66],"(hDS-RNN).":[67],"Our":[68],"model":[69,112],"consists":[70],"two":[72],"stages:":[73],"encoder":[77],"decoder.":[82],"Specifically,":[83],"attention":[87],"mechanism":[88],"that":[89,110],"employs":[90],"inputs":[91],"along":[92],"axes":[97],"proposed.":[99],"Experiments":[100],"on":[101],"real-world":[103],"data":[104],"set":[105],"conducted,":[107],"which":[108],"demonstrate":[109],"our":[111],"outperformed":[113],"seven":[114],"baseline":[115],"models":[116],"in":[117,122],"predictions":[121],"WDS.":[123]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
