{"id":"https://openalex.org/W3036590791","doi":null,"title":"A deep convolutional neural network for rapid fluvial flood inundation modelling","display_name":"A deep convolutional neural network for rapid fluvial flood inundation modelling","publication_year":2020,"publication_date":"2020-06-20","ids":{"openalex":"https://openalex.org/W3036590791","mag":"3036590791"},"language":"en","primary_location":{"id":"pmh:oai:research-information.bris.ac.uk:openaire_cris_publications/325c8c6b-86d9-41d1-9516-fa314eab4e88","doi":null,"is_oa":false,"landing_page_url":"https://research-information.bris.ac.uk/en/publications/325c8c6b-86d9-41d1-9516-fa314eab4e88","pdf_url":null,"source":{"id":"https://openalex.org/S4306400895","display_name":"Bristol Research (University of Bristol)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I36234482","host_organization_name":"University of Bristol","host_organization_lineage":["https://openalex.org/I36234482"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Kabir, S, Patidar, S, Xia, X, Liang, Q, Neal, J & Pender, G 2020, 'A deep convolutional neural network for rapid fluvial flood inundation modelling', Journal of Hydrology, vol. 590, 125481.","raw_type":"info:eu-repo/semantics/publishedVersion"},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2006.11555.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Syed Rezwan Kabir","orcid":"https://orcid.org/0000-0002-7820-4797","observed_orcids":["https://orcid.org/0000-0002-7820-4797"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Syed Rezwan Kabir","raw_affiliation_strings":["1 and 2"],"raw_orcid":"https://orcid.org/0000-0002-7820-4797","affiliations":[{"raw_affiliation_string":"1 and 2","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013165709","display_name":"Sandhya Patidar","orcid":"https://orcid.org/0000-0001-9562-6986","observed_orcids":["https://orcid.org/0000-0001-9562-6986"]},"institutions":[{"id":"https://openalex.org/I32062511","display_name":"Heriot-Watt University","ror":"https://ror.org/04mghma93","country_code":"GB","type":"education","lineage":["https://openalex.org/I32062511"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Sandhya Patidar","raw_affiliation_strings":["School of Energy, Geoscience, Infrastructure and Society Heriot\u2010Watt University Edinburgh United Kingdom"],"raw_orcid":"https://orcid.org/0000-0001-9562-6986","affiliations":[{"raw_affiliation_string":"School of Energy, Geoscience, Infrastructure and Society Heriot\u2010Watt University Edinburgh United Kingdom","institution_ids":["https://openalex.org/I32062511"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060779282","display_name":"Xilin Xia","orcid":"https://orcid.org/0000-0002-5784-9211","observed_orcids":["https://orcid.org/0000-0002-5784-9211"]},"institutions":[{"id":"https://openalex.org/I143804889","display_name":"Loughborough University","ror":"https://ror.org/04vg4w365","country_code":"GB","type":"education","lineage":["https://openalex.org/I143804889"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Xilin Xia","raw_affiliation_strings":["School of Architecture Building and Civil engineering Loughborough University Loughborough United Kingdom"],"raw_orcid":"https://orcid.org/0000-0002-5784-9211","affiliations":[{"raw_affiliation_string":"School of Architecture Building and Civil engineering Loughborough University Loughborough United Kingdom","institution_ids":["https://openalex.org/I143804889"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002926942","display_name":"Qiuhua Liang","orcid":"https://orcid.org/0000-0003-3223-6344","observed_orcids":["https://orcid.org/0000-0003-3223-6344"]},"institutions":[{"id":"https://openalex.org/I143804889","display_name":"Loughborough University","ror":"https://ror.org/04vg4w365","country_code":"GB","type":"education","lineage":["https://openalex.org/I143804889"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Qiuhua Liang","raw_affiliation_strings":["School of Architecture Building and Civil engineering Loughborough University Loughborough United Kingdom"],"raw_orcid":"https://orcid.org/0000-0003-3223-6344","affiliations":[{"raw_affiliation_string":"School of Architecture Building and Civil engineering Loughborough University Loughborough United Kingdom","institution_ids":["https://openalex.org/I143804889"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086031620","display_name":"Jeffrey Charles Neal","orcid":"https://orcid.org/0000-0001-5793-9594","observed_orcids":["https://orcid.org/0000-0001-5793-9594"]},"institutions":[{"id":"https://openalex.org/I36234482","display_name":"University of Bristol","ror":"https://ror.org/0524sp257","country_code":"GB","type":"education","lineage":["https://openalex.org/I36234482"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Jeffrey Neal","raw_affiliation_strings":["School of Geographical Sciences, University of Bristol, Bristol, United Kingdom"],"raw_orcid":"https://orcid.org/0000-0001-5793-9594","affiliations":[{"raw_affiliation_string":"School of Geographical Sciences, University of Bristol, Bristol, United Kingdom","institution_ids":["https://openalex.org/I36234482"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050462722","display_name":"Gareth Pender","orcid":"https://orcid.org/0000-0003-2265-8228","observed_orcids":["https://orcid.org/0000-0003-2265-8228"]},"institutions":[{"id":"https://openalex.org/I32062511","display_name":"Heriot-Watt University","ror":"https://ror.org/04mghma93","country_code":"GB","type":"education","lineage":["https://openalex.org/I32062511"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Gareth Pender","raw_affiliation_strings":["School of Energy, Geoscience, Infrastructure and Society Heriot\u2010Watt University Edinburgh United Kingdom"],"raw_orcid":"https://orcid.org/0000-0003-2265-8228","affiliations":[{"raw_affiliation_string":"School of Energy, Geoscience, Infrastructure and Society Heriot\u2010Watt University Edinburgh United Kingdom","institution_ids":["https://openalex.org/I32062511"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1389,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.44076082,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":93},"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/T10930","display_name":"Flood Risk Assessment and Management","score":0.7139000296592712,"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":0.7139000296592712,"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":0.2809999883174896,"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/T11206","display_name":"Model Reduction and Neural Networks","score":0.0006000000284984708,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/flood-inundation-modeling","display_name":"flood inundation modeling","score":0.9269999861717224},{"id":"https://openalex.org/keywords/deep-convolutional-neural-networks","display_name":"deep convolutional neural networks","score":0.8550000190734863},{"id":"https://openalex.org/keywords/river-floods","display_name":"river flooding","score":0.7689999938011169},{"id":"https://openalex.org/keywords/water-depth-prediction","display_name":"water depth prediction","score":0.828000009059906},{"id":"https://openalex.org/keywords/lisflood-fp","display_name":"LISFLOOD-FP","score":0.9440000057220459},{"id":"https://openalex.org/keywords/two-dimensional-hydraulic-model","display_name":"two-dimensional hydraulic model","score":0.6579999923706055},{"id":"https://openalex.org/keywords/support-vector-regression","display_name":"support vector regression","score":0.7850000262260437},{"id":"https://openalex.org/keywords/real-time-flood-forecasting","display_name":"real-time flood forecasting","score":0.9200000166893005},{"id":"https://openalex.org/keywords/computational-efficiency","display_name":"computational efficiency","score":0.5339999794960022}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7746117115020752},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7158053517341614},{"id":"https://openalex.org/C74256435","wikidata":"https://www.wikidata.org/wiki/Q134052","display_name":"Flood myth","level":2,"score":0.6802400350570679},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.5688733458518982},{"id":"https://openalex.org/C186594467","wikidata":"https://www.wikidata.org/wiki/Q1429176","display_name":"Flooding (psychology)","level":2,"score":0.5619039535522461},{"id":"https://openalex.org/C112959462","wikidata":"https://www.wikidata.org/wiki/Q1434465","display_name":"Fluvial","level":3,"score":0.5037757754325867},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.46655553579330444},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4625984728336334},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.41577935218811035},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.30646541714668274},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.18944579362869263},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11079615354537964},{"id":"https://openalex.org/C27206212","wikidata":"https://www.wikidata.org/wiki/Q34178","display_name":"Theology","level":1,"score":0.0},{"id":"https://openalex.org/C109007969","wikidata":"https://www.wikidata.org/wiki/Q749565","display_name":"Structural basin","level":2,"score":0.0},{"id":"https://openalex.org/C542102704","wikidata":"https://www.wikidata.org/wiki/Q183257","display_name":"Psychotherapist","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:research-information.bris.ac.uk:openaire_cris_publications/325c8c6b-86d9-41d1-9516-fa314eab4e88","doi":null,"is_oa":false,"landing_page_url":"https://research-information.bris.ac.uk/en/publications/325c8c6b-86d9-41d1-9516-fa314eab4e88","pdf_url":null,"source":{"id":"https://openalex.org/S4306400895","display_name":"Bristol Research (University of Bristol)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I36234482","host_organization_name":"University of Bristol","host_organization_lineage":["https://openalex.org/I36234482"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Kabir, S, Patidar, S, Xia, X, Liang, Q, Neal, J & Pender, G 2020, 'A deep convolutional neural network for rapid fluvial flood inundation modelling', Journal of Hydrology, vol. 590, 125481.","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"mag:3036590791","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/2006.11555.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null}],"best_oa_location":{"id":"mag:3036590791","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/2006.11555.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},"sustainable_development_goals":[{"score":0.6618000268936157,"display_name":"Clean water and sanitation","id":"https://metadata.un.org/sdg/6"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W126245055","https://openalex.org/W1437335841","https://openalex.org/W1481185622","https://openalex.org/W1543844559","https://openalex.org/W1562836990","https://openalex.org/W1694119402","https://openalex.org/W1831853112","https://openalex.org/W1925824783","https://openalex.org/W1970360626","https://openalex.org/W1981410107","https://openalex.org/W2033904036","https://openalex.org/W2035871099","https://openalex.org/W2037702125","https://openalex.org/W2046935534","https://openalex.org/W2049818605","https://openalex.org/W2055668893","https://openalex.org/W2078957148","https://openalex.org/W2101044141","https://openalex.org/W2106411961","https://openalex.org/W2131241448","https://openalex.org/W2141786991","https://openalex.org/W2163605009","https://openalex.org/W2166309152","https://openalex.org/W2194982918","https://openalex.org/W2223222085","https://openalex.org/W2291961022","https://openalex.org/W2537146596","https://openalex.org/W2596245284","https://openalex.org/W2735758519","https://openalex.org/W2756789966","https://openalex.org/W2765264620","https://openalex.org/W2802835092","https://openalex.org/W2803875552","https://openalex.org/W2889736303","https://openalex.org/W2890946365","https://openalex.org/W2903988118","https://openalex.org/W2907855851","https://openalex.org/W2911964244","https://openalex.org/W2916291895","https://openalex.org/W2944356707","https://openalex.org/W2945377681","https://openalex.org/W2965598025","https://openalex.org/W3003917887","https://openalex.org/W3024645822","https://openalex.org/W3032082602"],"related_works":["https://openalex.org/W3083014107","https://openalex.org/W4200376405","https://openalex.org/W4397034367","https://openalex.org/W7160163689","https://openalex.org/W4312053133","https://openalex.org/W4387722614","https://openalex.org/W4405623838","https://openalex.org/W4414267858","https://openalex.org/W4212971015","https://openalex.org/W4313407401"],"abstract_inverted_index":{"The":[0,28,46,65,77,92,105,145],"two-dimensional":[1,37],"(2D)":[2],"hydrodynamic":[3],"models":[4],"are":[5,67],"often":[6],"infeasible":[7],"for":[8,23,125,133,151],"real-time":[9,152],"operations.":[10],"In":[11],"this":[12],"paper,":[13],"a":[14,36,86,102],"deep":[15],"convolutional":[16],"neural":[17],"network":[18],"(CNN)-based":[19],"method":[20,147],"is":[21,31,49,82,107,130,143],"presented":[22],"rapid":[24],"fluvial":[25],"flood":[26,128],"modelling.":[27],"CNN":[29,81,97],"model":[30,39,48,98,106],"trained":[32],"using":[33],"outputs":[34,71],"from":[35],"hydraulic":[38],"(i.e.":[40],"LISFLOOD-FP)":[41],"to":[42,52],"predict":[43],"water":[44],"depths.":[45],"pre-trained":[47],"then":[50],"applied":[51],"simulate":[53],"the":[54,70,74,80,96,122,126,137],"flooding":[55],"event":[56],"that":[57,95],"occurred":[58],"in":[59,62,110],"Carlisle,":[60],"UK,":[61],"January":[63],"2005.":[64],"predictions":[66],"compared":[68,84],"against":[69],"produced":[72],"by":[73,101,116],"calibrated":[75],"LISFLOOD-FP.":[76],"performance":[78,158],"of":[79,136],"also":[83],"with":[85],"support":[87],"vector":[88],"regression":[89],"(SVR)-based":[90],"method.":[91],"results":[93],"show":[94],"outperforms":[99],"SVR":[100],"large":[103],"margin.":[104],"highly":[108],"accurate":[109],"capturing":[111],"flooded":[112],"cells":[113,135],"as":[114],"indicated":[115],"several":[117],"quantitative":[118],"assessment":[119],"matrices,":[120],"e.g.,":[121],"estimated":[123],"error":[124],"peak":[127],"depth":[129],"0-0.2":[131],"meters":[132],"97%":[134],"domain":[138],"when":[139],"99%":[140],"confidence":[141],"level":[142],"drawn.":[144],"proposed":[146],"offers":[148],"great":[149],"potential":[150],"applications":[153],"considering":[154],"its":[155],"simplicity,":[156],"superior":[157],"and":[159],"computational":[160],"efficiency.":[161]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2020-06-25T00:00:00"}