{"id":"https://openalex.org/W4406495714","doi":"https://doi.org/10.1109/bigdata62323.2024.10825210","title":"Forecasting Nutrient Flows using Terrain Elevation-aware Spatial-Temporal Graph Neural Networks","display_name":"Forecasting Nutrient Flows using Terrain Elevation-aware Spatial-Temporal Graph Neural Networks","publication_year":2024,"publication_date":"2024-12-15","ids":{"openalex":"https://openalex.org/W4406495714","doi":"https://doi.org/10.1109/bigdata62323.2024.10825210"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata62323.2024.10825210","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825210","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","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/A5100325979","display_name":"Jiaqi Li","orcid":"https://orcid.org/0009-0000-9552-1244"},"institutions":[{"id":"https://openalex.org/I58956616","display_name":"Case Western Reserve University","ror":"https://ror.org/051fd9666","country_code":"US","type":"education","lineage":["https://openalex.org/I58956616"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Jiaqi Li","raw_affiliation_strings":["Case Western Reserve University,Dept. Computer and Data Sciences,Cleveland,USA"],"affiliations":[{"raw_affiliation_string":"Case Western Reserve University,Dept. Computer and Data Sciences,Cleveland,USA","institution_ids":["https://openalex.org/I58956616"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071093153","display_name":"Yinghui Wu","orcid":"https://orcid.org/0000-0003-3991-5155"},"institutions":[{"id":"https://openalex.org/I58956616","display_name":"Case Western Reserve University","ror":"https://ror.org/051fd9666","country_code":"US","type":"education","lineage":["https://openalex.org/I58956616"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yinghui Wu","raw_affiliation_strings":["Case Western Reserve University,Dept. Computer and Data Sciences,Cleveland,USA"],"affiliations":[{"raw_affiliation_string":"Case Western Reserve University,Dept. Computer and Data Sciences,Cleveland,USA","institution_ids":["https://openalex.org/I58956616"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046462944","display_name":"A. Bradley","orcid":"https://orcid.org/0009-0003-4482-0748"},"institutions":[{"id":"https://openalex.org/I58956616","display_name":"Case Western Reserve University","ror":"https://ror.org/051fd9666","country_code":"US","type":"education","lineage":["https://openalex.org/I58956616"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alexandar Harding Bradley","raw_affiliation_strings":["Case Western Reserve University,Dept. Computer and Data Sciences,Cleveland,USA"],"affiliations":[{"raw_affiliation_string":"Case Western Reserve University,Dept. Computer and Data Sciences,Cleveland,USA","institution_ids":["https://openalex.org/I58956616"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112527930","display_name":"Olatunde Akanbi","orcid":null},"institutions":[{"id":"https://openalex.org/I58956616","display_name":"Case Western Reserve University","ror":"https://ror.org/051fd9666","country_code":"US","type":"education","lineage":["https://openalex.org/I58956616"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Olatunde Akanbi","raw_affiliation_strings":["Case Western Reserve University,Dept. Material Science and Engineering,Cleveland,USA"],"affiliations":[{"raw_affiliation_string":"Case Western Reserve University,Dept. Material Science and Engineering,Cleveland,USA","institution_ids":["https://openalex.org/I58956616"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041373135","display_name":"Erika I. Barcelos","orcid":"https://orcid.org/0000-0002-9273-8488"},"institutions":[{"id":"https://openalex.org/I58956616","display_name":"Case Western Reserve University","ror":"https://ror.org/051fd9666","country_code":"US","type":"education","lineage":["https://openalex.org/I58956616"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Erika I. Barcelos","raw_affiliation_strings":["Case Western Reserve University,Dept. Material Science and Engineering,Cleveland,USA"],"affiliations":[{"raw_affiliation_string":"Case Western Reserve University,Dept. Material Science and Engineering,Cleveland,USA","institution_ids":["https://openalex.org/I58956616"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078601277","display_name":"Laura S. Bruckman","orcid":"https://orcid.org/0000-0003-1271-1072"},"institutions":[{"id":"https://openalex.org/I58956616","display_name":"Case Western Reserve University","ror":"https://ror.org/051fd9666","country_code":"US","type":"education","lineage":["https://openalex.org/I58956616"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Laura S. Bruckman","raw_affiliation_strings":["Case Western Reserve University,Dept. Material Science and Engineering,Cleveland,USA"],"affiliations":[{"raw_affiliation_string":"Case Western Reserve University,Dept. Material Science and Engineering,Cleveland,USA","institution_ids":["https://openalex.org/I58956616"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085904418","display_name":"Roger H. French","orcid":"https://orcid.org/0000-0002-6162-0532"},"institutions":[{"id":"https://openalex.org/I58956616","display_name":"Case Western Reserve University","ror":"https://ror.org/051fd9666","country_code":"US","type":"education","lineage":["https://openalex.org/I58956616"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Roger H. French","raw_affiliation_strings":["Case Western Reserve University,Dept. Material Science and Engineering,Cleveland,USA"],"affiliations":[{"raw_affiliation_string":"Case Western Reserve University,Dept. Material Science and Engineering,Cleveland,USA","institution_ids":["https://openalex.org/I58956616"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5100325979"],"corresponding_institution_ids":["https://openalex.org/I58956616"],"apc_list":null,"apc_paid":null,"fwci":0.5263,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.67614654,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"8748","last_page":"8750"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.995199978351593,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.995199978351593,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/T11106","display_name":"Data Management and Algorithms","score":0.9905999898910522,"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/T12120","display_name":"Air Quality Monitoring and Forecasting","score":0.9807000160217285,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/terrain","display_name":"Terrain","score":0.7873724102973938},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7067947387695312},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4840823709964752},{"id":"https://openalex.org/keywords/elevation","display_name":"Elevation (ballistics)","score":0.475511372089386},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4632861018180847},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3625044822692871},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.15424618124961853},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.14663070440292358},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.11617287993431091},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09224674105644226}],"concepts":[{"id":"https://openalex.org/C161840515","wikidata":"https://www.wikidata.org/wiki/Q186131","display_name":"Terrain","level":2,"score":0.7873724102973938},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7067947387695312},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4840823709964752},{"id":"https://openalex.org/C37054046","wikidata":"https://www.wikidata.org/wiki/Q641888","display_name":"Elevation (ballistics)","level":2,"score":0.475511372089386},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4632861018180847},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3625044822692871},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.15424618124961853},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.14663070440292358},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.11617287993431091},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09224674105644226},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata62323.2024.10825210","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825210","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5400000214576721,"display_name":"Climate action","id":"https://metadata.un.org/sdg/13"},{"score":0.49000000953674316,"display_name":"Zero hunger","id":"https://metadata.un.org/sdg/2"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":3,"referenced_works":["https://openalex.org/W2001461008","https://openalex.org/W2998681661","https://openalex.org/W4379537566"],"related_works":["https://openalex.org/W4399671601","https://openalex.org/W1992962589","https://openalex.org/W3032871857","https://openalex.org/W1743191351","https://openalex.org/W3104633800","https://openalex.org/W2069184433","https://openalex.org/W3023567978","https://openalex.org/W3044778482","https://openalex.org/W2914059119","https://openalex.org/W4386906247"],"abstract_inverted_index":{"Spatiotemporal":[0],"graph":[1,40,90],"neural":[2],"networks":[3],"(STGNNs)":[4],"have":[5],"been":[6],"adopted":[7],"for":[8,53,78,181],"predictive":[9],"analysis":[10],"in":[11,121],"various":[12],"scientific":[13],"domains.":[14],"Despite":[15],"their":[16],"promising":[17,161],"performance,":[18],"the":[19,128,136,190],"dominance":[20],"of":[21,39,123,135,143,194],"big":[22],"(geo)spatiotemporal":[23],"data":[24,48,87,107,120,130,144],"with":[25,92,111,160],"large":[26],"and":[27,37,44,68,81,84,89,114,156,176,192],"heterogeneous":[28],"dimensions":[29],"raise":[30],"computational":[31],"challenges":[32],"to":[33,100,139,145,173,178],"effective":[34],"adoption,":[35],"generalization":[36],"fine-tuning":[38],"models.":[41],"Moreover,":[42],"continuous":[43],"high":[45],"quality":[46],"historical":[47,64],"may":[49],"not":[50],"always":[51],"exist":[52],"such":[54],"generalization.":[55,183],"This":[56,168],"paper":[57],"proposes":[58],"a":[59,93,132,141,150],"framework":[60],"that":[61,96],"can":[62],"co-evolve":[63],"geospatial":[65,86],"temporal":[66],"datasets":[67],"an":[69],"STGNN":[70],"model":[71,138],"by":[72,131,165],"(1)":[73,116],"incorporating":[74],"elevation":[75],"features":[76],"optimized":[77],"water":[79],"systems,":[80],"(2)":[82,126],"integrating":[83],"interacting":[85],"discovery":[88],"learning":[91],"\"rehearsal\"":[94],"mechanism,":[95],"automatically":[97],"generalize":[98,158,179],"STGNNs":[99,159],"broader":[101],"areas.":[102],"The":[103],"process":[104,170],"divides":[105],"spatiotemporal":[106],"into":[108],"regional":[109,162],"fragments":[110],"inferrable":[112],"features,":[113],"iteratively":[115],"augment":[117],"sparse":[118],"training":[119],"terms":[122],"feature":[124],"similarity,":[125],"explore":[127],"augmented":[129],"trial":[133],"\"rehearsing\"":[134],"current":[137],"decide":[140,174],"fraction":[142],"be":[146],"adopted,":[147],"over":[148],"which":[149],"consistently":[151],"good":[152],"accuracy":[153],"is":[154],"observed,":[155],"(3)":[157],"data,":[163],"ensured":[164],"rehearsal":[166,196],"performance.":[167],"exploratory":[169],"hence":[171],"learns":[172],"when":[175],"where":[177],"STGNNs,":[180],"cost-effective":[182],"Using":[184],"real-world":[185],"datasets,":[186],"we":[187],"experimentally":[188],"verify":[189],"effectiveness":[191],"efficiency":[193],"our":[195],"framework.":[197]},"counts_by_year":[{"year":2026,"cited_by_count":2}],"updated_date":"2026-04-03T22:45:19.894376","created_date":"2025-10-10T00:00:00"}