{"id":"https://openalex.org/W4406458038","doi":"https://doi.org/10.1109/bigdata62323.2024.10825269","title":"Groundwater Level Prediction: Analyzing the Performance of LSTM and QLSTM Model","display_name":"Groundwater Level Prediction: Analyzing the Performance of LSTM and QLSTM Model","publication_year":2024,"publication_date":"2024-12-15","ids":{"openalex":"https://openalex.org/W4406458038","doi":"https://doi.org/10.1109/bigdata62323.2024.10825269"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata62323.2024.10825269","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825269","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/A5088544470","display_name":"Aditi Saha","orcid":"https://orcid.org/0009-0003-3134-1406"},"institutions":[{"id":"https://openalex.org/I6026837","display_name":"Tuskegee University","ror":"https://ror.org/0137n4m74","country_code":"US","type":"education","lineage":["https://openalex.org/I6026837"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Aditi Saha","raw_affiliation_strings":["Tuskegee University,Computer Science,Tuskegee,USA"],"affiliations":[{"raw_affiliation_string":"Tuskegee University,Computer Science,Tuskegee,USA","institution_ids":["https://openalex.org/I6026837"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056510846","display_name":"Mohammad Rahman","orcid":"https://orcid.org/0009-0008-9324-3728"},"institutions":[{"id":"https://openalex.org/I6026837","display_name":"Tuskegee University","ror":"https://ror.org/0137n4m74","country_code":"US","type":"education","lineage":["https://openalex.org/I6026837"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mohammad Rahman","raw_affiliation_strings":["Tuskegee University,Computer Science,Tuskegee,USA"],"affiliations":[{"raw_affiliation_string":"Tuskegee University,Computer Science,Tuskegee,USA","institution_ids":["https://openalex.org/I6026837"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038803920","display_name":"Fan Wu","orcid":"https://orcid.org/0000-0003-2797-4692"},"institutions":[{"id":"https://openalex.org/I6026837","display_name":"Tuskegee University","ror":"https://ror.org/0137n4m74","country_code":"US","type":"education","lineage":["https://openalex.org/I6026837"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fan Wu","raw_affiliation_strings":["Tuskegee University,Computer Science,Tuskegee,USA"],"affiliations":[{"raw_affiliation_string":"Tuskegee University,Computer Science,Tuskegee,USA","institution_ids":["https://openalex.org/I6026837"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5088544470"],"corresponding_institution_ids":["https://openalex.org/I6026837"],"apc_list":null,"apc_paid":null,"fwci":0.8212,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.70925968,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"3755","last_page":"3763"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11490","display_name":"Hydrological Forecasting Using AI","score":0.9948999881744385,"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":0.9948999881744385,"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/T10320","display_name":"Neural Networks and Applications","score":0.944100022315979,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9424999952316284,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6649079918861389},{"id":"https://openalex.org/keywords/groundwater","display_name":"Groundwater","score":0.4797281324863434},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.38246798515319824},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.18571257591247559},{"id":"https://openalex.org/keywords/geotechnical-engineering","display_name":"Geotechnical engineering","score":0.10862836241722107}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6649079918861389},{"id":"https://openalex.org/C76177295","wikidata":"https://www.wikidata.org/wiki/Q161598","display_name":"Groundwater","level":2,"score":0.4797281324863434},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38246798515319824},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.18571257591247559},{"id":"https://openalex.org/C187320778","wikidata":"https://www.wikidata.org/wiki/Q1349130","display_name":"Geotechnical engineering","level":1,"score":0.10862836241722107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata62323.2024.10825269","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825269","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":[{"id":"https://metadata.un.org/sdg/6","display_name":"Clean water and sanitation","score":0.7099999785423279}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1566427759","https://openalex.org/W1929158363","https://openalex.org/W2064675550","https://openalex.org/W2070685888","https://openalex.org/W2072282618","https://openalex.org/W2099743616","https://openalex.org/W2106371033","https://openalex.org/W2147541497","https://openalex.org/W2402268235","https://openalex.org/W2737855739","https://openalex.org/W2793350103","https://openalex.org/W2885195348","https://openalex.org/W2896827527","https://openalex.org/W2913280151","https://openalex.org/W2944851425","https://openalex.org/W2955325220","https://openalex.org/W2982145560","https://openalex.org/W3046711798","https://openalex.org/W3082455048","https://openalex.org/W4220727972","https://openalex.org/W4285391032","https://openalex.org/W4289606390","https://openalex.org/W4367333149","https://openalex.org/W4395689810","https://openalex.org/W4399695505","https://openalex.org/W4400980763","https://openalex.org/W4403272521","https://openalex.org/W6741543560","https://openalex.org/W6755964158","https://openalex.org/W6870391454"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"This":[0],"study":[1,35,102,124],"uses":[2],"a":[3],"multi-core,":[4],"high-performance":[5,41],"computer":[6],"system":[7],"to":[8,79,106],"evaluate":[9],"the":[10,48,81,107,129,149,156,161],"performance":[11],"of":[12,100,114,131],"Quantum":[13],"Long":[14],"Short-Term":[15],"Memory":[16],"(QLSTM)":[17],"and":[18,65,83,95,134,142],"classical":[19],"LSTM":[20,84,143,157],"models":[21,85],"for":[22,139,164],"predicting":[23,160],"groundwater":[24,28,115,162],"levels.":[25],"Leveraging":[26],"historical":[27],"data":[29,45],"from":[30],"different":[31,118],"monitoring":[32,56],"sites,":[33],"this":[34,101,123],"conducts":[36],"extensive":[37],"numerical":[38],"experiments":[39],"in":[40,73,159],"workstations.":[42],"Groundwater":[43],"level":[44,163],"collected":[46],"by":[47,110],"United":[49],"States":[50],"Geological":[51],"Survey":[52],"(USGS)":[53],"at":[54,117],"two":[55],"stations,":[57],"i.e.,":[58],"Champion":[59],"Well":[60],"referred":[61,68],"as":[62,69,89],"site":[63,70],"1":[64],"Pisgah":[66],"Forest":[67],"2,":[71],"located":[72],"North":[74],"Carolina,":[75],"has":[76],"been":[77],"used":[78],"train":[80],"QLSTM":[82,141,150],"leveraging":[86],"features":[87],"such":[88],"evapotranspiration,":[90],"precipitation,":[91],"temperature,":[92],"surface":[93],"pressure,":[94],"wind":[96],"speed.":[97],"The":[98],"outcome":[99],"provides":[103,126,135],"distinct":[104],"benefits":[105],"meteorological":[108],"community":[109],"enabling":[111],"rapid":[112],"prediction":[113],"levels":[116],"locations.":[119],"Besides":[120],"model":[121,151,158],"accuracy,":[122],"also":[125],"insights":[127],"into":[128],"optimization":[130],"computational":[132],"resources":[133],"an":[136],"effective":[137],"platform":[138],"deploying":[140],"algorithms.":[144],"Our":[145],"finding":[146],"suggests":[147],"that":[148],"performs":[152],"comparatively":[153],"better":[154],"than":[155],"rapidly":[165],"changing":[166],"environmental":[167],"conditions.":[168]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3}],"updated_date":"2026-03-31T07:56:22.981413","created_date":"2025-10-10T00:00:00"}