{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T15:32:06Z","timestamp":1784820726229,"version":"3.55.0"},"reference-count":50,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100000996","name":"Australian Government\u2019s Department of Foreign Affairs and Trade through the Australia Awards Scholarship scheme for Fiji","doi-asserted-by":"publisher","award":["2020-2021"],"award-info":[{"award-number":["2020-2021"]}],"id":[{"id":"10.13039\/501100000996","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/access.2021.3065939","type":"journal-article","created":{"date-parts":[[2021,3,15]],"date-time":"2021-03-15T20:16:51Z","timestamp":1615839411000},"page":"50982-50993","source":"Crossref","is-referenced-by-count":181,"title":["Designing Deep-Based Learning Flood Forecast Model With ConvLSTM Hybrid Algorithm"],"prefix":"10.1109","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9673-1398","authenticated-orcid":false,"given":"Mohammed","family":"Moishin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2290-6749","authenticated-orcid":false,"given":"Ravinesh C.","family":"Deo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3941-5839","authenticated-orcid":false,"given":"Ramendra","family":"Prasad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8364-2644","authenticated-orcid":false,"given":"Nawin","family":"Raj","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1193-6969","authenticated-orcid":false,"given":"Shahab","family":"Abdulla","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","author":"boschetti","year":"2015","journal-title":"Python Data Science Essentials"},{"key":"ref38","first-page":"57","article-title":"Python: A programming language for software integration and development","volume":"17","author":"sanner","year":"1999","journal-title":"J Mol Graph Model"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.4337\/9781781001806.00031"},{"key":"ref32","first-page":"155","article-title":"Support vector regression machines","author":"drucker","year":"1997","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-020-04867-x"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.3390\/w11071387"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.2151\/jmsj.80.33"},{"key":"ref36","year":"2019","journal-title":"Matlab"},{"key":"ref35","first-page":"1","article-title":"Flooding in the Fiji Islands between 1840 and 2009","author":"mcgree","year":"2010","journal-title":"Risk Fronter"},{"key":"ref34","article-title":"Climate change vulnerability and adaptation assessment for Fiji","author":"feresi","year":"2000"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/s10661-018-6806-0"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/s11269-015-1046-3"},{"key":"ref29","article-title":"Anomaly detection using predictive convolutional long short term memory units","author":"medel","year":"2016"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-7717.2010.01163.x"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1080\/17565529.2016.1174656"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1029\/1998WR900086"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.2166\/hydro.2007.027"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1029\/2004WR003562"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.5626\/JOK.2017.44.6.607"},{"key":"ref23","article-title":"Decentralized flood forecasting using deep neural networks","author":"sit","year":"2019","journal-title":"arXiv 1902 02308"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1029\/2009GL038817"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2011.04.012"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.3390\/hydrology5040066"},{"key":"ref10","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"lecun","year":"2015","journal-title":"Nature"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref40","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"pedregosa","year":"2011","journal-title":"J Mach Learn Res"},{"key":"ref12","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1007\/978-3-642-24797-2_4","article-title":"Long short-term memory","author":"graves","year":"2012","journal-title":"Supervised Sequence Labelling with Recurrent Neural Networks"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.12.129"},{"key":"ref14","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1007\/978-3-642-40585-3_14","article-title":"Application of LSTM neural networks in language modelling","author":"soutner","year":"2013","journal-title":"Text Speech and Dialogue"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.3390\/w10111543"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2019.05.230"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/WCSP.2017.8171119"},{"key":"ref18","first-page":"802","article-title":"Convolutional LSTM network: A machine learning approach for precipitation nowcasting","author":"xingjian","year":"2015","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref19","first-page":"1","article-title":"Practical applications of the standardised precipitation index (SPI) as a tool for very early warning of droughts and floods in the Balkans region","volume":"18","author":"faulkner","year":"2016","journal-title":"Proc EGU General Assembly Conf Abstr"},{"key":"ref4","first-page":"657","article-title":"Quantified diagnosis of flood possibility by using effective precipitation index","volume":"31","author":"byeon","year":"1998","journal-title":"Journal of Korea Water Resources Association"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/s11269-015-1046-3"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/s00704-018-2657-4"},{"key":"ref5","first-page":"ap20-1","article-title":"Diagnosis of flood events in Brisbane (Australia) using a flood index based on daily effective precipitation","author":"deo","year":"2014","journal-title":"Proc Int Conf Anal Manage Changing Risks Natural Hazards Eur Commission 7th Framework Programme Marie Curie Actions"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-95991-5_127"},{"key":"ref49","article-title":"ThibHlln\/hydroeval: General enhancements","author":"hallouin","year":"2019"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/s00477-020-01899-6"},{"key":"ref9","article-title":"Urban flood prediction in real-time from weather radar and rainfall data using artificial neural networks","author":"duncan","year":"2011","journal-title":"Proc Weather Radar Hydrol Int Symp (WRAH)"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-30211-5_3"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1080\/07350015.1995.10524601"},{"key":"ref48","author":"brownlee","year":"2021","journal-title":"How to Develop LSTM Models for Time Series Forecasting"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1007\/s000240050038"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4842-2766-4_7"},{"key":"ref41","first-page":"265","article-title":"Tensorflow: A system for large-scale machine learning","author":"abadi","year":"2016","journal-title":"Proc of USENIX Symp on Operating Systems Design and Implementation (OSDI)"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/60.3.613"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2019.113541"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9312710\/09378529.pdf?arnumber=9378529","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,26]],"date-time":"2022-01-26T17:40:01Z","timestamp":1643218801000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9378529\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":50,"URL":"https:\/\/doi.org\/10.1109\/access.2021.3065939","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]}}}