{"id":"https://openalex.org/W2893478583","doi":"https://doi.org/10.2312/envirvis.20181135","title":"Predict Saturated Thickness using TensorBoard Visualization","display_name":"Predict Saturated Thickness using TensorBoard Visualization","publication_year":2018,"publication_date":"2018-01-01","ids":{"openalex":"https://openalex.org/W2893478583","doi":"https://doi.org/10.2312/envirvis.20181135","mag":"2893478583"},"language":"en","primary_location":{"id":"doi:10.2312/envirvis.20181135","is_oa":true,"landing_page_url":"https://doi.org/10.2312/envirvis.20181135","pdf_url":null,"source":{"id":"https://openalex.org/S7407052899","display_name":"Eurographics","issn_l":null,"issn":[],"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":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"article","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.2312/envirvis.20181135","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Nguyen, Vinh The","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nguyen, Vinh The","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032280607","display_name":"Tommy Dang","orcid":"https://orcid.org/0000-0001-8322-0014"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dang, Tommy","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5101706801","display_name":"Fang Jin","orcid":"https://orcid.org/0000-0002-6606-5232"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jin, Fang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13650","display_name":"Computational Physics and Python Applications","score":0.23280000686645508,"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"}},"topics":[{"id":"https://openalex.org/T13650","display_name":"Computational Physics and Python Applications","score":0.23280000686645508,"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/T11366","display_name":"Elasticity and Material Modeling","score":0.20810000598430634,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.2029999941587448,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.5585668683052063},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4171467423439026},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.35131019353866577},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.23513805866241455}],"concepts":[{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.5585668683052063},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4171467423439026},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.35131019353866577},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.23513805866241455}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.2312/envirvis.20181135","is_oa":true,"landing_page_url":"https://doi.org/10.2312/envirvis.20181135","pdf_url":null,"source":{"id":"https://openalex.org/S7407052899","display_name":"Eurographics","issn_l":null,"issn":[],"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":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.2312/envirvis.20181135","is_oa":true,"landing_page_url":"https://doi.org/10.2312/envirvis.20181135","pdf_url":null,"source":{"id":"https://openalex.org/S7407052899","display_name":"Eurographics","issn_l":null,"issn":[],"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":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"display_name":"Climate action","id":"https://metadata.un.org/sdg/13","score":0.5}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2371010926","https://openalex.org/W2140376300","https://openalex.org/W1043907963","https://openalex.org/W44831003","https://openalex.org/W2387945540","https://openalex.org/W1994465268","https://openalex.org/W2113172002","https://openalex.org/W2151530263","https://openalex.org/W2078899744","https://openalex.org/W2617468327","https://openalex.org/W3206856967","https://openalex.org/W2373264576","https://openalex.org/W2351622305","https://openalex.org/W3085976556","https://openalex.org/W2110570486","https://openalex.org/W1599511619","https://openalex.org/W1555495800","https://openalex.org/W2245166612","https://openalex.org/W2100301518","https://openalex.org/W2507083727"],"abstract_inverted_index":{"Water":[0],"plays":[1],"a":[2,21,86,106,116],"critical":[3],"role":[4],"in":[5,73,80],"our":[6],"living":[7],"and":[8,25,82,114,124],"manufacturing":[9],"activities.":[10],"The":[11,91],"continuously":[12],"growing":[13],"exploitation":[14],"of":[15,52,70,96],"water":[16,38,49],"over":[17],"the":[18,59,67,74,101,126],"aquifer":[19,37,72],"poses":[20],"risk":[22],"for":[23,88],"over-extraction":[24],"pollution,":[26],"leading":[27],"to":[28,65,120],"many":[29],"negative":[30],"effects":[31],"on":[32],"land":[33],"irrigation.":[34],"Therefore,":[35],"predicting":[36],"level":[39],"accurately":[40],"is":[41],"urgently":[42],"important,":[43],"which":[44],"can":[45,104],"help":[46,121],"us":[47,122],"prepare":[48],"demands":[50],"ahead":[51],"time.":[53],"In":[54],"this":[55,97],"study,":[56],"we":[57],"employ":[58],"Long-Short":[60],"Term":[61],"Memory":[62],"(LSTM)":[63],"model":[64,89,103,127],"predict":[66],"saturated":[68],"thickness":[69],"an":[71],"Southern":[75],"High":[76],"Plains":[77],"Aquifer":[78],"System":[79],"Texas,":[81],"exploit":[83],"TensorBoard":[84],"as":[85],"guide":[87],"configurations.":[90],"Root":[92],"Mean":[93],"Squared":[94],"Error":[95],"study":[98],"shows":[99],"that":[100],"LSTM":[102],"provide":[105],"good":[107,117],"prediction":[108],"capability":[109],"using":[110],"multiple":[111],"data":[112],"sources,":[113],"provides":[115],"visualization":[118],"tool":[119],"understand":[123],"evaluate":[125],"configuration.":[128]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":3}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2018-10-05T00:00:00"}