{"id":"https://openalex.org/W4290948144","doi":"https://doi.org/10.1145/3534678.3539115","title":"Physics-Guided Graph Meta Learning for Predicting Water Temperature and Streamflow in Stream Networks","display_name":"Physics-Guided Graph Meta Learning for Predicting Water Temperature and Streamflow in Stream Networks","publication_year":2022,"publication_date":"2022-08-12","ids":{"openalex":"https://openalex.org/W4290948144","doi":"https://doi.org/10.1145/3534678.3539115"},"language":"en","primary_location":{"id":"doi:10.1145/3534678.3539115","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3534678.3539115","pdf_url":null,"source":{"id":"https://openalex.org/S4363608767","display_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-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/A5103181412","display_name":"Shengyu Chen","orcid":"https://orcid.org/0009-0006-9576-9841"},"institutions":[{"id":"https://openalex.org/I170201317","display_name":"University of Pittsburgh","ror":"https://ror.org/01an3r305","country_code":"US","type":"education","lineage":["https://openalex.org/I170201317"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Shengyu Chen","raw_affiliation_strings":["University of Pittsburgh, Pittsburgh, PA, USA"],"affiliations":[{"raw_affiliation_string":"University of Pittsburgh, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I170201317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005617262","display_name":"Jacob A. Zwart","orcid":"https://orcid.org/0000-0002-3870-405X"},"institutions":[{"id":"https://openalex.org/I1286329397","display_name":"United States Geological Survey","ror":"https://ror.org/035a68863","country_code":"US","type":"funder","lineage":["https://openalex.org/I1286329397","https://openalex.org/I1335927249"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jacob A. Zwart","raw_affiliation_strings":["U.S. Geological Survey, Pittsburgh, PA, USA"],"affiliations":[{"raw_affiliation_string":"U.S. Geological Survey, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I1286329397"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001445783","display_name":"Xiaowei Jia","orcid":"https://orcid.org/0000-0001-8544-5233"},"institutions":[{"id":"https://openalex.org/I170201317","display_name":"University of Pittsburgh","ror":"https://ror.org/01an3r305","country_code":"US","type":"education","lineage":["https://openalex.org/I170201317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaowei Jia","raw_affiliation_strings":["University of Pittsburgh, Pittsburgh, PA, USA"],"affiliations":[{"raw_affiliation_string":"University of Pittsburgh, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I170201317"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5103181412"],"corresponding_institution_ids":["https://openalex.org/I170201317"],"apc_list":null,"apc_paid":null,"fwci":10.4482,"has_fulltext":false,"cited_by_count":19,"citation_normalized_percentile":{"value":0.99021821,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"2752","last_page":"2761"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10330","display_name":"Hydrology and Watershed Management Studies","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/2312","display_name":"Water Science and Technology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10330","display_name":"Hydrology and Watershed Management Studies","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/2312","display_name":"Water Science and Technology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11490","display_name":"Hydrological Forecasting Using AI","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10930","display_name":"Flood Risk Assessment and Management","score":0.9954000115394592,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/streamflow","display_name":"Streamflow","score":0.6538164615631104},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.646495521068573},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5870928168296814},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5348109006881714},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.43976637721061707},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.39802250266075134},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.38839736580848694},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3280787467956543},{"id":"https://openalex.org/keywords/drainage-basin","display_name":"Drainage basin","score":0.2057928442955017},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.1335897147655487},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.0844724178314209}],"concepts":[{"id":"https://openalex.org/C53739315","wikidata":"https://www.wikidata.org/wiki/Q29425295","display_name":"Streamflow","level":3,"score":0.6538164615631104},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.646495521068573},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5870928168296814},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5348109006881714},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.43976637721061707},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39802250266075134},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38839736580848694},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3280787467956543},{"id":"https://openalex.org/C126645576","wikidata":"https://www.wikidata.org/wiki/Q166620","display_name":"Drainage basin","level":2,"score":0.2057928442955017},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.1335897147655487},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0844724178314209},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3534678.3539115","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3534678.3539115","pdf_url":null,"source":{"id":"https://openalex.org/S4363608767","display_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Clean water and sanitation","score":0.8600000143051147,"id":"https://metadata.un.org/sdg/6"}],"awards":[{"id":"https://openalex.org/G327174889","display_name":null,"funder_award_id":"2147195","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W200110011","https://openalex.org/W2033904036","https://openalex.org/W2081346522","https://openalex.org/W2136180243","https://openalex.org/W2151382875","https://openalex.org/W2156159004","https://openalex.org/W2175931243","https://openalex.org/W2255930315","https://openalex.org/W2259147390","https://openalex.org/W2461251725","https://openalex.org/W2582343773","https://openalex.org/W2753990673","https://openalex.org/W2783598392","https://openalex.org/W2807021761","https://openalex.org/W2911286998","https://openalex.org/W2965857891","https://openalex.org/W3094624443","https://openalex.org/W3100848837","https://openalex.org/W3102903231","https://openalex.org/W3167924209","https://openalex.org/W4206733017","https://openalex.org/W4225747436","https://openalex.org/W4242892226","https://openalex.org/W4243596117","https://openalex.org/W6892160308","https://openalex.org/W6967313402"],"related_works":["https://openalex.org/W2439644404","https://openalex.org/W3174343427","https://openalex.org/W2000979301","https://openalex.org/W98325756","https://openalex.org/W2061279527","https://openalex.org/W105199006","https://openalex.org/W4213385761","https://openalex.org/W2335811955","https://openalex.org/W2804771885","https://openalex.org/W3183229010"],"abstract_inverted_index":{"This":[0],"paper":[1],"proposes":[2],"a":[3,59,116],"graph-based":[4],"meta":[5],"learning":[6,46,61],"approach":[7],"to":[8,67,81],"separately":[9],"predict":[10],"water":[11,25,105],"quantity":[12],"and":[13,85,107],"quality":[14],"variables":[15,131],"for":[16,52,89,102,109],"river":[17,83],"segments":[18,84],"in":[19,28,128],"stream":[20,42,54],"networks.":[21],"Given":[22],"the":[23,69,87,90,96,99,110,123],"heterogeneous":[24],"dynamic":[26],"patterns":[27],"large-scale":[29],"basins,":[30],"we":[31,57],"introduce":[32],"an":[33],"additional":[34],"meta-learning":[35,91],"condition":[36,88],"based":[37],"on":[38],"physical":[39,65,70],"characteristics":[40,71],"of":[41,49,72,98,125,150],"segments,":[43],"which":[44],"allows":[45],"different":[47,53,148],"sets":[48],"initial":[50],"parameters":[51],"segments.":[55],"Specifically,":[56],"develop":[58],"representation":[60],"method":[62,101,127,142],"that":[63,140],"leverages":[64],"simulations":[66],"embed":[68],"each":[73],"segment.":[74],"The":[75,120],"obtained":[76],"embeddings":[77],"are":[78],"then":[79],"used":[80],"cluster":[82],"add":[86],"process.":[92],"We":[93,137],"have":[94],"tested":[95],"performance":[97,146],"proposed":[100],"predicting":[103,129],"daily":[104],"temperature":[106],"streamflow":[108],"Delaware":[111],"River":[112],"Basin":[113],"(DRB)":[114],"over":[115],"14":[117],"year":[118],"period.":[119],"results":[121],"confirm":[122],"effectiveness":[124],"our":[126,141],"target":[130],"even":[132],"using":[133],"sparse":[134],"training":[135],"samples.":[136],"also":[138],"show":[139],"can":[143],"achieve":[144],"robust":[145],"with":[147],"numbers":[149],"clusterings.":[151]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1}],"updated_date":"2026-03-12T08:34:05.389933","created_date":"2025-10-10T00:00:00"}
