{"id":"https://openalex.org/W4312443912","doi":"https://doi.org/10.1109/igarss46834.2022.9884091","title":"Runoff Prediction in a Data Scarce Region Based on Few-Shot Learning","display_name":"Runoff Prediction in a Data Scarce Region Based on Few-Shot Learning","publication_year":2022,"publication_date":"2022-07-17","ids":{"openalex":"https://openalex.org/W4312443912","doi":"https://doi.org/10.1109/igarss46834.2022.9884091"},"language":"en","primary_location":{"id":"doi:10.1109/igarss46834.2022.9884091","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss46834.2022.9884091","pdf_url":null,"source":{"id":"https://openalex.org/S4363604196","display_name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","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":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","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/A5101733679","display_name":"Minghong Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Minghong Yang","raw_affiliation_strings":["School of Resources and Environment, University of Electronic Science and Technology of China,Chengdu,China,611731"],"affiliations":[{"raw_affiliation_string":"School of Resources and Environment, University of Electronic Science and Technology of China,Chengdu,China,611731","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015410049","display_name":"Qinli Yang","orcid":"https://orcid.org/0000-0001-8966-6762"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qinli Yang","raw_affiliation_strings":["School of Resources and Environment, University of Electronic Science and Technology of China,Chengdu,China,611731"],"affiliations":[{"raw_affiliation_string":"School of Resources and Environment, University of Electronic Science and Technology of China,Chengdu,China,611731","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088843448","display_name":"Junming Shao","orcid":"https://orcid.org/0000-0002-1183-2991"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junming Shao","raw_affiliation_strings":["School of Computer Science and Engineering, University of Electronic Science and Technology of China,Chengdu,China,611731"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, University of Electronic Science and Technology of China,Chengdu,China,611731","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100334021","display_name":"Guoqing Wang","orcid":"https://orcid.org/0000-0002-9121-9571"},"institutions":[{"id":"https://openalex.org/I4210111986","display_name":"Nanjing Hydraulic Research Institute","ror":"https://ror.org/02403qw73","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210111986"]},{"id":"https://openalex.org/I4210099662","display_name":"State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering","ror":"https://ror.org/012wsxz85","country_code":"CN","type":"facility","lineage":["https://openalex.org/I163340411","https://openalex.org/I4210099662","https://openalex.org/I4210111986","https://openalex.org/I4210120069","https://openalex.org/I4210127390"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guoqing Wang","raw_affiliation_strings":["Nanjing Hydraulic Research Institute,State Key Laboratory of Hydrology- Water Resources and Hydraulic Engineering,Nanjing,China,210029"],"affiliations":[{"raw_affiliation_string":"Nanjing Hydraulic Research Institute,State Key Laboratory of Hydrology- Water Resources and Hydraulic Engineering,Nanjing,China,210029","institution_ids":["https://openalex.org/I4210099662","https://openalex.org/I4210111986"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101733679"],"corresponding_institution_ids":["https://openalex.org/I150229711"],"apc_list":null,"apc_paid":null,"fwci":0.6455,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.62560778,"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":"6304","last_page":"6307"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11490","display_name":"Hydrological Forecasting Using AI","score":0.9997000098228455,"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":0.9997000098228455,"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/T10330","display_name":"Hydrology and Watershed Management Studies","score":0.9995999932289124,"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/T10930","display_name":"Flood Risk Assessment and Management","score":0.9970999956130981,"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/computer-science","display_name":"Computer science","score":0.6809710264205933},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.6298544406890869},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5686043500900269},{"id":"https://openalex.org/keywords/surface-runoff","display_name":"Surface runoff","score":0.5605742931365967},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.5477187633514404},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.5336310267448425},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5282988548278809},{"id":"https://openalex.org/keywords/shot","display_name":"Shot (pellet)","score":0.47396013140678406},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.43202081322669983},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.42942214012145996},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10096824169158936},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.08026981353759766}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6809710264205933},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.6298544406890869},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5686043500900269},{"id":"https://openalex.org/C50477045","wikidata":"https://www.wikidata.org/wiki/Q1444790","display_name":"Surface runoff","level":2,"score":0.5605742931365967},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.5477187633514404},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.5336310267448425},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5282988548278809},{"id":"https://openalex.org/C2778344882","wikidata":"https://www.wikidata.org/wiki/Q278938","display_name":"Shot (pellet)","level":2,"score":0.47396013140678406},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.43202081322669983},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.42942214012145996},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10096824169158936},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.08026981353759766},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss46834.2022.9884091","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss46834.2022.9884091","pdf_url":null,"source":{"id":"https://openalex.org/S4363604196","display_name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","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":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2872276652","display_name":null,"funder_award_id":"2021YFC3201100","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G7539523448","display_name":null,"funder_award_id":"52079026,41830863,61976044","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"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W2545772354","https://openalex.org/W2601450892","https://openalex.org/W2766801343","https://openalex.org/W2804523998","https://openalex.org/W2963845150","https://openalex.org/W2972371821","https://openalex.org/W2997307506","https://openalex.org/W2997637170","https://openalex.org/W6735236233","https://openalex.org/W6772506417"],"related_works":["https://openalex.org/W4312200629","https://openalex.org/W4309045103","https://openalex.org/W4225161397","https://openalex.org/W3014300295","https://openalex.org/W4299487748","https://openalex.org/W3164822677","https://openalex.org/W4250304930","https://openalex.org/W4223943233","https://openalex.org/W2806031239","https://openalex.org/W2979536015"],"abstract_inverted_index":{"In":[0],"this":[1],"study,":[2,42],"we":[3,43],"propose":[4],"a":[5,40],"new":[6],"method":[7],"(metric-LSTM":[8],"fusion":[9],"model)":[10],"for":[11,96,123],"runoff":[12,61,124],"prediction":[13,62,125],"in":[14,126],"data":[15,52,94],"scarce":[16],"regions":[17],"by":[18],"combining":[19],"few-shot":[20,116],"learning":[21,117],"and":[22],"deep":[23],"learning.":[24],"To":[25],"validate":[26],"the":[27,30,34,70,76,87,91,93,99,102,105,111],"effectiveness":[28],"of":[29,49,83,104],"proposed":[31,71,106,112],"model,":[32],"taking":[33],"upper":[35],"Yellow":[36],"River":[37],"basin":[38],"as":[39],"case":[41],"compare":[44],"its":[45],"performance":[46],"with":[47,75],"that":[48,69,110],"four":[50],"state-of-the-art":[51],"driven":[53],"models":[54],"(LSTM,":[55],"SVR,":[56],"RF,":[57],"ANN)":[58],"on":[59,115],"monthly":[60],"during":[63],"1970":[64],"to":[65],"1995.":[66],"Results":[67],"indicate":[68],"model":[72,80,97,113],"performs":[73],"well":[74],"best":[77],"NSE":[78],"(Nash-Sutcliffe":[79],"efficiency":[81],"coefficient)":[82],"0.83,":[84],"outperforming":[85],"all":[86],"comparative":[88],"models.":[89],"Furthermore,":[90],"less":[92],"used":[95],"training,":[98],"more":[100],"obvious":[101],"advance":[103],"model.":[107],"Findings":[108],"imply":[109],"based":[114],"can":[118],"provide":[119],"an":[120],"effective":[121],"tool":[122],"data-scarce":[127],"regions.":[128]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}