{"id":"https://openalex.org/W4376612884","doi":"https://doi.org/10.1007/s00500-023-08254-1","title":"Deep learning algorithm development for river flow prediction: PNP algorithm","display_name":"Deep learning algorithm development for river flow prediction: PNP algorithm","publication_year":2023,"publication_date":"2023-05-13","ids":{"openalex":"https://openalex.org/W4376612884","doi":"https://doi.org/10.1007/s00500-023-08254-1"},"language":"en","primary_location":{"id":"doi:10.1007/s00500-023-08254-1","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s00500-023-08254-1","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00500-023-08254-1.pdf","source":{"id":"https://openalex.org/S65753830","display_name":"Soft Computing","issn_l":"1432-7643","issn":["1432-7643","1433-7479"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Soft Computing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s00500-023-08254-1.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5077560442","display_name":"Gwi-Man Bak","orcid":"https://orcid.org/0000-0003-1219-2751"},"institutions":[{"id":"https://openalex.org/I111277659","display_name":"Chonnam National University","ror":"https://ror.org/05kzjxq56","country_code":"KR","type":"education","lineage":["https://openalex.org/I111277659"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Gwiman Bak","raw_affiliation_strings":["Department of Electrical and Semiconductor, Chonnam National University, Yeosu, 59626, Korea"],"raw_orcid":"https://orcid.org/0000-0003-1219-2751","affiliations":[{"raw_affiliation_string":"Department of Electrical and Semiconductor, Chonnam National University, Yeosu, 59626, Korea","institution_ids":["https://openalex.org/I111277659"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085351058","display_name":"Young-Chul Bae","orcid":"https://orcid.org/0000-0003-3184-9667"},"institutions":[{"id":"https://openalex.org/I111277659","display_name":"Chonnam National University","ror":"https://ror.org/05kzjxq56","country_code":"KR","type":"education","lineage":["https://openalex.org/I111277659"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Youngchul Bae","raw_affiliation_strings":["Division of Electrical and Computer Engineering, Chonnam National University, Yeosu, 59626, Korea"],"raw_orcid":"https://orcid.org/0000-0003-3184-9667","affiliations":[{"raw_affiliation_string":"Division of Electrical and Computer Engineering, Chonnam National University, Yeosu, 59626, Korea","institution_ids":["https://openalex.org/I111277659"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5085351058"],"corresponding_institution_ids":["https://openalex.org/I111277659"],"apc_list":{"value":2390,"currency":"EUR","value_usd":2990},"apc_paid":{"value":2390,"currency":"EUR","value_usd":2990},"fwci":1.4734,"has_fulltext":true,"cited_by_count":13,"citation_normalized_percentile":{"value":0.79362601,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"27","issue":"18","first_page":"13487","last_page":"13515"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11490","display_name":"Hydrological Forecasting Using AI","score":0.9993000030517578,"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":0.9993000030517578,"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":0.9812999963760376,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9703999757766724,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.822576642036438},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.682475745677948},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6300958395004272},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6113946437835693},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5919243693351746},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.49892163276672363},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.4820295572280884},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.44180402159690857},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.4105513393878937},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.2577129006385803}],"concepts":[{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.822576642036438},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.682475745677948},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6300958395004272},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6113946437835693},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5919243693351746},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.49892163276672363},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.4820295572280884},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.44180402159690857},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.4105513393878937},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2577129006385803}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/s00500-023-08254-1","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s00500-023-08254-1","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00500-023-08254-1.pdf","source":{"id":"https://openalex.org/S65753830","display_name":"Soft Computing","issn_l":"1432-7643","issn":["1432-7643","1433-7479"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Soft Computing","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1007/s00500-023-08254-1","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s00500-023-08254-1","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00500-023-08254-1.pdf","source":{"id":"https://openalex.org/S65753830","display_name":"Soft Computing","issn_l":"1432-7643","issn":["1432-7643","1433-7479"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Soft Computing","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.7900000214576721,"display_name":"Climate action","id":"https://metadata.un.org/sdg/13"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4376612884.pdf"},"referenced_works_count":42,"referenced_works":["https://openalex.org/W1547372245","https://openalex.org/W1726806267","https://openalex.org/W1998582365","https://openalex.org/W2011582941","https://openalex.org/W2040870580","https://openalex.org/W2064675550","https://openalex.org/W2077623746","https://openalex.org/W2103212315","https://openalex.org/W2105969173","https://openalex.org/W2110485445","https://openalex.org/W2116360511","https://openalex.org/W2119554284","https://openalex.org/W2120012334","https://openalex.org/W2131774270","https://openalex.org/W2141125852","https://openalex.org/W2883986738","https://openalex.org/W2890088836","https://openalex.org/W2904560462","https://openalex.org/W2909194804","https://openalex.org/W2944607176","https://openalex.org/W2949562060","https://openalex.org/W2953521532","https://openalex.org/W2967757476","https://openalex.org/W2980994438","https://openalex.org/W2989057107","https://openalex.org/W2993767981","https://openalex.org/W3002262829","https://openalex.org/W3035665735","https://openalex.org/W3038584219","https://openalex.org/W3040266635","https://openalex.org/W3135550350","https://openalex.org/W3158135283","https://openalex.org/W3171884590","https://openalex.org/W3183475563","https://openalex.org/W3196897376","https://openalex.org/W3204448112","https://openalex.org/W4205581908","https://openalex.org/W4224218492","https://openalex.org/W4308970516","https://openalex.org/W6600175266","https://openalex.org/W6603242443","https://openalex.org/W6604896550"],"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":{"Abstract":[0],"Deep":[1],"learning":[2,62,78,161,172,190],"algorithms":[3,63,79,102,113,234],"developed":[4,119],"in":[5,11,105],"recent":[6],"decades":[7],"have":[8],"performed":[9,229],"well":[10],"prediction":[12],"and":[13,52,99,111,131,137,166,219,225],"classification":[14],"using":[15,60,149,174,192,213],"accumulated":[16],"big":[17,66],"data.":[18],"However,":[19],"as":[20,50,93],"climate":[21,150],"change":[22],"has":[23],"recently":[24],"become":[25],"a":[26,44,135,158],"more":[27],"serious":[28],"global":[29],"problem,":[30],"natural":[31,38,75,122],"disasters":[32,39],"are":[33,48],"occurring":[34],"frequently.":[35],"When":[36],"analyzing":[37],"from":[40],"the":[41,53,94,97,126,129,144,175,180,199,202,207,214,232],"perspective":[42],"of":[43,147,157,164,182,201,210],"data":[45,67],"analyst,":[46],"they":[47],"considered":[49],"outliers,":[51],"ability":[54],"to":[55,83,86,120,142,178,197],"predict":[56,74,87,121,143,179],"outliers":[57,88],"(natural":[58],"disasters)":[59],"deep":[61,77,160,171,189],"based":[64,89],"on":[65,90],"acquired":[68],"by":[69],"computers":[70],"is":[71],"limited.":[72],"To":[73],"disasters,":[76],"must":[80,108,117],"be":[81,84,109,118],"enhanced":[82],"able":[85],"information":[91],"such":[92],"correlation":[95,127],"between":[96,128],"input":[98,130],"output.":[100],"Thus,":[101],"that":[103,227],"specialize":[104],"one":[106],"field":[107],"developed,":[110],"specialized":[112],"for":[114],"abnormal":[115],"values":[116],"disasters.":[123],"Therefore,":[124],"considering":[125],"output,":[132],"we":[133],"propose":[134],"positive":[136,165],"negative":[138,167],"perceptron":[139],"(PNP)":[140],"algorithm":[141,155,177],"flow":[145,181],"rate":[146],"rivers":[148],"change-sensitive":[151],"precipitation.":[152],"The":[153],"PNP":[154,176,203],"consists":[156],"hidden":[159],"layer":[162],"composed":[163],"neurons.":[168],"We":[169,185,205],"built":[170,187],"models":[173,191],"three":[183],"rivers.":[184],"also":[186],"comparative":[188],"long":[193],"short-term":[194],"memory":[195],"(LSTM)":[196],"validate":[198],"performance":[200,209],"algorithm.":[204],"compared":[206],"predictive":[208],"each":[211],"model":[212],"root":[215],"mean":[216,221],"square":[217],"error":[218,224],"symmetric":[220],"absolute":[222],"percentage":[223],"demonstrated":[226],"it":[228],"better":[230],"than":[231],"LSTM":[233],".":[235]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":1}],"updated_date":"2026-06-13T06:13:01.061226","created_date":"2025-10-10T00:00:00"}
