{"id":"https://openalex.org/W3207130222","doi":"https://doi.org/10.1109/igarss47720.2021.9553957","title":"Hydrological Big Data Prediction Based on Shared Weight Long Short-Term Memory","display_name":"Hydrological Big Data Prediction Based on Shared Weight Long Short-Term Memory","publication_year":2021,"publication_date":"2021-07-11","ids":{"openalex":"https://openalex.org/W3207130222","doi":"https://doi.org/10.1109/igarss47720.2021.9553957","mag":"3207130222"},"language":"en","primary_location":{"id":"doi:10.1109/igarss47720.2021.9553957","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss47720.2021.9553957","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS","raw_type":"proceedings-article"},"type":"conference-paper","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/A5101482288","display_name":"Rui Wang","orcid":"https://orcid.org/0000-0003-2609-4255"},"institutions":[{"id":"https://openalex.org/I163340411","display_name":"Hohai University","ror":"https://ror.org/01wd4xt90","country_code":"CN","type":"education","lineage":["https://openalex.org/I163340411"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rui Wang","raw_affiliation_strings":["College of Computer and Information, Hohai University, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer and Information, Hohai University, Nanjing, China","institution_ids":["https://openalex.org/I163340411"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008159697","display_name":"Ding sheng Wan","orcid":null},"institutions":[{"id":"https://openalex.org/I163340411","display_name":"Hohai University","ror":"https://ror.org/01wd4xt90","country_code":"CN","type":"education","lineage":["https://openalex.org/I163340411"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ding sheng Wan","raw_affiliation_strings":["College of Computer and Information, Hohai University, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer and Information, Hohai University, Nanjing, China","institution_ids":["https://openalex.org/I163340411"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100343458","display_name":"Ke Li","orcid":"https://orcid.org/0000-0001-8372-0350"},"institutions":[{"id":"https://openalex.org/I163340411","display_name":"Hohai University","ror":"https://ror.org/01wd4xt90","country_code":"CN","type":"education","lineage":["https://openalex.org/I163340411"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ke Li","raw_affiliation_strings":["College of Computer and Information, Hohai University, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer and Information, Hohai University, Nanjing, China","institution_ids":["https://openalex.org/I163340411"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I163340411"],"apc_list":null,"apc_paid":null,"fwci":0.4728,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.57038429,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"5787","last_page":"5790"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11490","display_name":"Hydrological Forecasting Using AI","score":0.9994000196456909,"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"}},"topics":[{"id":"https://openalex.org/T11490","display_name":"Hydrological Forecasting Using AI","score":0.9994000196456909,"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.9975000023841858,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T10330","display_name":"Hydrology and Watershed Management Studies","score":0.9919000267982483,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.8390681743621826},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6701908111572266},{"id":"https://openalex.org/keywords/flood-myth","display_name":"Flood myth","score":0.5740580558776855},{"id":"https://openalex.org/keywords/long-short-term-memory","display_name":"Long short term memory","score":0.4611170291900635},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.4546627402305603},{"id":"https://openalex.org/keywords/structural-basin","display_name":"Structural basin","score":0.422598659992218},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4070560336112976},{"id":"https://openalex.org/keywords/hydrology","display_name":"Hydrology (agriculture)","score":0.32784420251846313},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.217288076877594},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.21626877784729004},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.10845470428466797},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.08953496813774109},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.07544377446174622}],"concepts":[{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.8390681743621826},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6701908111572266},{"id":"https://openalex.org/C74256435","wikidata":"https://www.wikidata.org/wiki/Q134052","display_name":"Flood myth","level":2,"score":0.5740580558776855},{"id":"https://openalex.org/C133488467","wikidata":"https://www.wikidata.org/wiki/Q6673524","display_name":"Long short term memory","level":4,"score":0.4611170291900635},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.4546627402305603},{"id":"https://openalex.org/C109007969","wikidata":"https://www.wikidata.org/wiki/Q749565","display_name":"Structural basin","level":2,"score":0.422598659992218},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4070560336112976},{"id":"https://openalex.org/C76886044","wikidata":"https://www.wikidata.org/wiki/Q2883300","display_name":"Hydrology (agriculture)","level":2,"score":0.32784420251846313},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.217288076877594},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.21626877784729004},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.10845470428466797},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.08953496813774109},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.07544377446174622},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C187320778","wikidata":"https://www.wikidata.org/wiki/Q1349130","display_name":"Geotechnical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss47720.2021.9553957","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss47720.2021.9553957","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/6","display_name":"Clean water and sanitation","score":0.5899999737739563}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1967179053","https://openalex.org/W1969399508","https://openalex.org/W1978904808","https://openalex.org/W1997570574","https://openalex.org/W2039238531","https://openalex.org/W2052245711","https://openalex.org/W2086779092","https://openalex.org/W2132104490","https://openalex.org/W2136848157","https://openalex.org/W2920539417","https://openalex.org/W2972302268","https://openalex.org/W3021538729"],"related_works":["https://openalex.org/W4390608645","https://openalex.org/W4247566972","https://openalex.org/W2960264696","https://openalex.org/W3090563135","https://openalex.org/W2497432351","https://openalex.org/W4206777497","https://openalex.org/W4233347783","https://openalex.org/W2910064364","https://openalex.org/W4255224757","https://openalex.org/W2499527417"],"abstract_inverted_index":{"Hydrological":[0,22,104],"big":[1,20,36,57,94],"data":[2,12,37,58,101],"was":[3],"characterized":[4],"by":[5],"complexity":[6],"and":[7,31,41,67,88,124],"comprehensibility,":[8],"There":[9],"are":[10],"massive":[11],"association":[13,59],"relationships":[14],"to":[15,26,33,38,75],"be":[16],"mined":[17],"in":[18,29,109,111],"hydrological":[19,35,56,65,93],"data.":[21,95],"forecasting":[23],"is":[24,122],"primary":[25,47],"flood":[27],"prevention":[28],"China,":[30],"how":[32],"use":[34],"make":[39],"accurate":[40],"efficient":[42,123],"prediction":[43,90],"has":[44],"become":[45],"the":[46,53,62,77,83,97],"study.":[48],"This":[49],"study":[50],"starts":[51],"with":[52],"analysis":[54],"of":[55,64,79,86,92,102,106],"relationship,":[60],"captures":[61],"characteristics":[63],"data,":[66,115],"proposes":[68],"shared":[69],"weight":[70],"Long":[71],"Short-":[72],"Term":[73],"Memory(SWLSTM)":[74],"reduce":[76],"number":[78],"optimized":[80],"variables,":[81],"shorten":[82],"training":[84],"time":[85],"SWLSTM,":[87],"improve":[89],"accuracy":[91],"Taking":[96],"daily":[98],"water":[99],"level":[100],"Tunxi":[103,107],"Station":[105],"Basin":[108],"China":[110],"2016":[112],"as":[113],"experimental":[114,116],"results":[117],"demonstrate":[118],"that":[119],"our":[120],"approach":[121],"accurate.":[125]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
