{"id":"https://openalex.org/W4392185878","doi":"https://doi.org/10.1155/2024/3562709","title":"A Data-Driven Method and Hybrid Deep Learning Model for Flood Risk Prediction","display_name":"A Data-Driven Method and Hybrid Deep Learning Model for Flood Risk Prediction","publication_year":2024,"publication_date":"2024-02-27","ids":{"openalex":"https://openalex.org/W4392185878","doi":"https://doi.org/10.1155/2024/3562709"},"language":"en","primary_location":{"id":"doi:10.1155/2024/3562709","is_oa":true,"landing_page_url":"https://doi.org/10.1155/2024/3562709","pdf_url":"https://downloads.hindawi.com/journals/ijis/2024/3562709.pdf","source":{"id":"https://openalex.org/S57950554","display_name":"International Journal of Intelligent Systems","issn_l":"0884-8173","issn":["0884-8173","1098-111X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Intelligent Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://downloads.hindawi.com/journals/ijis/2024/3562709.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5064131498","display_name":"Chenmin Ni","orcid":"https://orcid.org/0000-0002-7865-4261"},"institutions":[{"id":"https://openalex.org/I139322472","display_name":"Universiti Sains Malaysia","ror":"https://ror.org/02rgb2k63","country_code":"MY","type":"education","lineage":["https://openalex.org/I139322472"]},{"id":"https://openalex.org/I4394709108","display_name":"Zhejiang Yuexiu University","ror":"https://ror.org/00scnsb87","country_code":null,"type":"education","lineage":["https://openalex.org/I4394709108"]}],"countries":["MY"],"is_corresponding":false,"raw_author_name":"Chenmin Ni","raw_affiliation_strings":["School of International Business, Zhejiang Yuexiu University, Shaoxing 312000, China","School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM Penang, Malaysia"],"raw_orcid":"https://orcid.org/0000-0002-7865-4261","affiliations":[{"raw_affiliation_string":"School of International Business, Zhejiang Yuexiu University, Shaoxing 312000, China","institution_ids":["https://openalex.org/I4394709108"]},{"raw_affiliation_string":"School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM Penang, Malaysia","institution_ids":["https://openalex.org/I139322472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063697101","display_name":"Fam Pei Shan","orcid":"https://orcid.org/0000-0002-1094-211X"},"institutions":[{"id":"https://openalex.org/I139322472","display_name":"Universiti Sains Malaysia","ror":"https://ror.org/02rgb2k63","country_code":"MY","type":"education","lineage":["https://openalex.org/I139322472"]}],"countries":["MY"],"is_corresponding":true,"raw_author_name":"Pei Shan Fam","raw_affiliation_strings":["School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM Penang, Malaysia"],"raw_orcid":"https://orcid.org/0000-0002-1094-211X","affiliations":[{"raw_affiliation_string":"School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM Penang, Malaysia","institution_ids":["https://openalex.org/I139322472"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5051758226","display_name":"Muhammad Fadhil Marsani","orcid":"https://orcid.org/0000-0003-4808-0348"},"institutions":[{"id":"https://openalex.org/I139322472","display_name":"Universiti Sains Malaysia","ror":"https://ror.org/02rgb2k63","country_code":"MY","type":"education","lineage":["https://openalex.org/I139322472"]}],"countries":["MY"],"is_corresponding":false,"raw_author_name":"Muhammad Fadhil Marsani","raw_affiliation_strings":["School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM Penang, Malaysia"],"raw_orcid":"https://orcid.org/0000-0003-4808-0348","affiliations":[{"raw_affiliation_string":"School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM Penang, Malaysia","institution_ids":["https://openalex.org/I139322472"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5063697101"],"corresponding_institution_ids":["https://openalex.org/I139322472"],"apc_list":{"value":2500,"currency":"USD","value_usd":2500},"apc_paid":{"value":2500,"currency":"USD","value_usd":2500},"fwci":3.5103,"has_fulltext":true,"cited_by_count":13,"citation_normalized_percentile":{"value":0.92549906,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"2024","issue":null,"first_page":"1","last_page":"20"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10930","display_name":"Flood Risk Assessment and Management","score":0.9983000159263611,"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"}},"topics":[{"id":"https://openalex.org/T10930","display_name":"Flood Risk Assessment and Management","score":0.9983000159263611,"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"}},{"id":"https://openalex.org/T11490","display_name":"Hydrological Forecasting Using AI","score":0.9954000115394592,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9952999949455261,"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/computer-science","display_name":"Computer science","score":0.6817681789398193},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5587721467018127},{"id":"https://openalex.org/keywords/flood-myth","display_name":"Flood myth","score":0.558039128780365},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4927275478839874},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4608120322227478},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.41390788555145264},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.0724274218082428}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6817681789398193},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5587721467018127},{"id":"https://openalex.org/C74256435","wikidata":"https://www.wikidata.org/wiki/Q134052","display_name":"Flood myth","level":2,"score":0.558039128780365},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4927275478839874},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4608120322227478},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.41390788555145264},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0724274218082428},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1155/2024/3562709","is_oa":true,"landing_page_url":"https://doi.org/10.1155/2024/3562709","pdf_url":"https://downloads.hindawi.com/journals/ijis/2024/3562709.pdf","source":{"id":"https://openalex.org/S57950554","display_name":"International Journal of Intelligent Systems","issn_l":"0884-8173","issn":["0884-8173","1098-111X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Intelligent Systems","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1155/2024/3562709","is_oa":true,"landing_page_url":"https://doi.org/10.1155/2024/3562709","pdf_url":"https://downloads.hindawi.com/journals/ijis/2024/3562709.pdf","source":{"id":"https://openalex.org/S57950554","display_name":"International Journal of Intelligent Systems","issn_l":"0884-8173","issn":["0884-8173","1098-111X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Intelligent Systems","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.5699999928474426,"id":"https://metadata.un.org/sdg/13","display_name":"Climate action"}],"awards":[{"id":"https://openalex.org/G6345915841","display_name":null,"funder_award_id":"FRGS/1/2022/STG06/USM/03/1","funder_id":"https://openalex.org/F4320321709","funder_display_name":"Ministry of Higher Education, Malaysia"}],"funders":[{"id":"https://openalex.org/F4320321709","display_name":"Ministry of Higher Education, Malaysia","ror":"https://ror.org/05mcs2t73"}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4392185878.pdf"},"referenced_works_count":52,"referenced_works":["https://openalex.org/W1820051232","https://openalex.org/W2000982976","https://openalex.org/W2044055120","https://openalex.org/W2064675550","https://openalex.org/W2331003978","https://openalex.org/W2772453098","https://openalex.org/W2914691113","https://openalex.org/W2936236242","https://openalex.org/W2947218679","https://openalex.org/W2963073938","https://openalex.org/W2998553334","https://openalex.org/W3003987289","https://openalex.org/W3034872464","https://openalex.org/W3040552057","https://openalex.org/W3043008751","https://openalex.org/W3108812200","https://openalex.org/W3116551709","https://openalex.org/W3127513234","https://openalex.org/W3135542251","https://openalex.org/W3137632581","https://openalex.org/W3143956158","https://openalex.org/W3171925258","https://openalex.org/W3172399786","https://openalex.org/W3176140884","https://openalex.org/W3200507375","https://openalex.org/W3209246221","https://openalex.org/W3209847991","https://openalex.org/W3213057073","https://openalex.org/W3215854402","https://openalex.org/W3216024987","https://openalex.org/W4205415841","https://openalex.org/W4205664112","https://openalex.org/W4210516877","https://openalex.org/W4213272346","https://openalex.org/W4229026125","https://openalex.org/W4281763510","https://openalex.org/W4283727603","https://openalex.org/W4286252298","https://openalex.org/W4289878199","https://openalex.org/W4292087306","https://openalex.org/W4292692261","https://openalex.org/W4292939874","https://openalex.org/W4297818472","https://openalex.org/W4309340877","https://openalex.org/W4317662941","https://openalex.org/W4318566923","https://openalex.org/W4318683721","https://openalex.org/W4362665370","https://openalex.org/W4383213725","https://openalex.org/W6638374719","https://openalex.org/W6682642761","https://openalex.org/W6696775231"],"related_works":["https://openalex.org/W2731899572","https://openalex.org/W2961085424","https://openalex.org/W3215138031","https://openalex.org/W4306674287","https://openalex.org/W3009238340","https://openalex.org/W4321369474","https://openalex.org/W4360585206","https://openalex.org/W4285208911","https://openalex.org/W3046775127","https://openalex.org/W3082895349"],"abstract_inverted_index":{"Flood":[0],"disasters":[1],"occur":[2],"worldwide,":[3],"and":[4,14,21,28,36,55,79,116,131,151,185,215,229],"flood":[5,42,46],"risk":[6],"prediction":[7],"is":[8,49,70,97,129,181,193],"conducive":[9],"to":[10,60,72,99,109,134],"protecting":[11],"human":[12],"life":[13],"property":[15],"safety.":[16],"Influenced":[17],"by":[18,211],"topographic":[19],"changes":[20],"rainfall,":[22],"the":[23,31,39,65,74,111,120,136,162,176,186,202,206,222,226],"water":[24,157,223],"level":[25,158,224],"fluctuates":[26,230],"randomly":[27],"violently":[29],"during":[30],"flood,":[32],"introducing":[33],"many":[34],"noises":[35],"directly":[37],"increasing":[38,119],"difficulty":[40],"of":[41,114,125,139,189],"prediction.":[43],"A":[44],"data-driven":[45],"forecasting":[47,220],"method":[48],"proposed":[50,142,203],"based":[51],"on":[52],"data":[53,75,159],"preprocessing":[54],"a":[56],"two-layer":[57,89],"BiLSTM-Attention":[58],"network":[59,96],"improve":[61],"forecast":[62],"accuracy.":[63],"First,":[64],"Variational":[66],"Mode":[67,83],"Decomposition":[68],"(VMD)":[69],"used":[71,108,133,168],"decompose":[73],"for":[76,147,169,218],"reducing":[77],"noise":[78],"produce":[80],"suitable":[81],"Intrinsic":[82],"Functions":[84],"(IMFs);":[85],"Then,":[86],"an":[87],"optimized":[88,112],"attention-based":[90],"Bidirectional":[91],"Long":[92],"Sshort-Term":[93],"memory":[94],"(BiLSTM-Attention)":[95],"constructed":[98],"predict":[100],"each":[101],"IMF.":[102],"Finally,":[103],"two":[104],"optimization":[105,128],"algorithms":[106],"are":[107,167],"obtain":[110],"parameters":[113],"VMD":[115],"BiLSTM":[117],"intelligently,":[118],"self-adaptability.":[121],"The":[122,141],"inertia":[123],"factor":[124],"particle":[126],"swarm":[127],"improved":[130],"then":[132],"optimize":[135],"five":[137],"hyperparameters":[138],"BiLSTM.":[140],"model":[143,204],"reduces":[144,205],"storage":[145],"errors":[146],"smaller":[148],"training":[149],"sets":[150,160],"can":[152],"achieve":[153],"good":[154],"performance.":[155],"Three":[156],"from":[161],"Yangtze":[163],"River":[164],"in":[165],"China":[166],"comparative":[170],"experiments.":[171],"Numerical":[172],"results":[173],"show":[174],"that":[175],"peak":[177,190],"height":[178],"absolute":[179],"error":[180,188,210],"within":[182,194],"2":[183],"cm,":[184],"relative":[187],"time":[191],"arrival":[192],"30%.":[195],"Compared":[196],"with":[197],"LSTM,":[198],"BiLSTM,":[199],"CNN-BiLSTM-attention,":[200],"etc.,":[201],"root":[207],"mean":[208],"square":[209],"at":[212],"least":[213],"50%":[214],"has":[216],"advantages":[217],"high-risk":[219],"when":[221],"exceeds":[225],"defense":[227],"line":[228],"prominently.":[231]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":2}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}