{"id":"https://openalex.org/W4291700538","doi":"https://doi.org/10.1109/icccs55155.2022.9846132","title":"A Hybrid Deep Learning Model for Water Supply Time Series Data Prediction","display_name":"A Hybrid Deep Learning Model for Water Supply Time Series Data Prediction","publication_year":2022,"publication_date":"2022-04-22","ids":{"openalex":"https://openalex.org/W4291700538","doi":"https://doi.org/10.1109/icccs55155.2022.9846132"},"language":"en","primary_location":{"id":"doi:10.1109/icccs55155.2022.9846132","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icccs55155.2022.9846132","pdf_url":null,"source":{"id":"https://openalex.org/S4363608130","display_name":"2022 7th International Conference on Computer and Communication Systems (ICCCS)","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":"2022 7th International Conference on Computer and Communication Systems (ICCCS)","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/A5002099577","display_name":"Jinfeng Teng","orcid":null},"institutions":[{"id":"https://openalex.org/I16365422","display_name":"Hefei University of Technology","ror":"https://ror.org/02czkny70","country_code":"CN","type":"education","lineage":["https://openalex.org/I16365422"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jinfeng Teng","raw_affiliation_strings":["Hefei University of Technology,School of Computer and Information,Hefei,China","School of Computer and Information, Hefei University of Technology, Hefei, China"],"affiliations":[{"raw_affiliation_string":"Hefei University of Technology,School of Computer and Information,Hefei,China","institution_ids":["https://openalex.org/I16365422"]},{"raw_affiliation_string":"School of Computer and Information, Hefei University of Technology, Hefei, China","institution_ids":["https://openalex.org/I16365422"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050080933","display_name":"Kaiwei Sheng","orcid":null},"institutions":[{"id":"https://openalex.org/I16365422","display_name":"Hefei University of Technology","ror":"https://ror.org/02czkny70","country_code":"CN","type":"education","lineage":["https://openalex.org/I16365422"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kaiwei Sheng","raw_affiliation_strings":["Hefei University of Technology,School of Computer and Information,Hefei,China","School of Computer and Information, Hefei University of Technology, Hefei, China"],"affiliations":[{"raw_affiliation_string":"Hefei University of Technology,School of Computer and Information,Hefei,China","institution_ids":["https://openalex.org/I16365422"]},{"raw_affiliation_string":"School of Computer and Information, Hefei University of Technology, Hefei, China","institution_ids":["https://openalex.org/I16365422"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5002099577"],"corresponding_institution_ids":["https://openalex.org/I16365422"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.10156672,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"180","last_page":"186"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11490","display_name":"Hydrological Forecasting Using AI","score":0.9973999857902527,"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":0.9973999857902527,"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":0.9919999837875366,"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/T12697","display_name":"Water Quality Monitoring Technologies","score":0.9905999898910522,"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/autoencoder","display_name":"Autoencoder","score":0.75315260887146},{"id":"https://openalex.org/keywords/water-supply","display_name":"Water supply","score":0.6487645506858826},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6268190741539001},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5914170145988464},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.5770394802093506},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.5445876121520996},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5428065061569214},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4899508059024811},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4572437107563019},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.441457599401474},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.43123704195022583},{"id":"https://openalex.org/keywords/modal","display_name":"Modal","score":0.42556095123291016},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3365418314933777},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.21667638421058655},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.09897449612617493},{"id":"https://openalex.org/keywords/environmental-engineering","display_name":"Environmental engineering","score":0.08016327023506165}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.75315260887146},{"id":"https://openalex.org/C97053079","wikidata":"https://www.wikidata.org/wiki/Q1061108","display_name":"Water supply","level":2,"score":0.6487645506858826},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6268190741539001},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5914170145988464},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.5770394802093506},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.5445876121520996},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5428065061569214},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4899508059024811},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4572437107563019},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.441457599401474},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43123704195022583},{"id":"https://openalex.org/C71139939","wikidata":"https://www.wikidata.org/wiki/Q910194","display_name":"Modal","level":2,"score":0.42556095123291016},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3365418314933777},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.21667638421058655},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.09897449612617493},{"id":"https://openalex.org/C87717796","wikidata":"https://www.wikidata.org/wiki/Q146326","display_name":"Environmental engineering","level":1,"score":0.08016327023506165},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","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/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C188027245","wikidata":"https://www.wikidata.org/wiki/Q750446","display_name":"Polymer chemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icccs55155.2022.9846132","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icccs55155.2022.9846132","pdf_url":null,"source":{"id":"https://openalex.org/S4363608130","display_name":"2022 7th International Conference on Computer and Communication Systems (ICCCS)","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":"2022 7th International Conference on Computer and Communication Systems (ICCCS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6200000047683716,"display_name":"Clean water and sanitation","id":"https://metadata.un.org/sdg/6"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320307000","display_name":"Manufacturing Institute","ror":"https://ror.org/027wewa02"},{"id":"https://openalex.org/F4320337504","display_name":"Research and Development","ror":"https://ror.org/027s68j25"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1498436455","https://openalex.org/W2008056655","https://openalex.org/W2058401212","https://openalex.org/W2064675550","https://openalex.org/W2080552614","https://openalex.org/W2295598076","https://openalex.org/W2521003103","https://openalex.org/W2805217154","https://openalex.org/W2898820347","https://openalex.org/W2910993421","https://openalex.org/W2917208276","https://openalex.org/W2917832751","https://openalex.org/W2953670495","https://openalex.org/W2979476417","https://openalex.org/W2984148598","https://openalex.org/W2996947682","https://openalex.org/W3005619874","https://openalex.org/W3100193987","https://openalex.org/W3121975202","https://openalex.org/W3217756247"],"related_works":["https://openalex.org/W2732415564","https://openalex.org/W4306194456","https://openalex.org/W4311257506","https://openalex.org/W3044458868","https://openalex.org/W2337926734","https://openalex.org/W4309045103","https://openalex.org/W4366224123","https://openalex.org/W4320802194","https://openalex.org/W4213225422","https://openalex.org/W4250304930"],"abstract_inverted_index":{"With":[0],"the":[1,24,78,87,101,108,113,121,131,144,158,161],"development":[2],"of":[3,9,116,160],"intelligent":[4],"water":[5,10,18,34,74,89,127],"services,":[6],"accurate":[7],"prediction":[8,45,154],"supply":[11,19,75,90,128],"data":[12,91],"plays":[13],"a":[14,42,148],"vital":[15],"role":[16],"in":[17,153],"safety":[20],"and":[21,38,57,68,98,111],"security.":[22],"At":[23],"same":[25],"time,":[26],"it":[27],"is":[28,49,83],"important":[29],"to":[30,85,106,119,150],"ensure":[31],"ecological":[32],"water,":[33],"for":[35,73],"people's":[36],"livelihood":[37],"industrial":[39],"water.":[40],"Therefore,":[41],"hybrid":[43],"multi-step":[44],"model":[46,60,146],"called":[47],"VMD-ConvGRUAE":[48,80,145],"proposed.":[50],"By":[51],"combining":[52],"variational":[53],"modal":[54,93],"decomposition":[55],"(VMD)":[56],"deep":[58,102],"autoencoder":[59],"based":[61],"on":[62,125],"convolutional":[63],"long":[64],"short-term":[65],"memory":[66],"(ConvLSTM)":[67],"gated":[69],"recurrent":[70],"unit":[71],"(GRU)":[72],"prediction.":[76],"In":[77],"proposed":[79],"model,":[81],"VMD":[82],"used":[84],"decompose":[86],"original":[88],"into":[92],"components":[94],"with":[95,139],"different":[96],"trends":[97],"then":[99],"combine":[100],"encoder-decoder":[103],"network":[104],"structure":[105],"predict":[107],"component":[109,118],"data,":[110],"add":[112],"predicted":[114,122],"results":[115],"each":[117],"obtain":[120],"value.":[123],"Experiments":[124],"real":[126],"datasets,":[129],"using":[130],"MAE":[132],"evaluation":[133],"metrics":[134],"as":[135],"an":[136],"example,":[137],"compared":[138],"several":[140],"other":[141],"baseline":[142],"methods,":[143],"has":[147],"4.34%":[149],"65.27%":[151],"improvement":[152],"accuracy,":[155],"which":[156],"verifies":[157],"validity":[159],"model.":[162]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}