{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T16:21:17Z","timestamp":1772814077539,"version":"3.50.1"},"reference-count":29,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"9","license":[{"start":{"date-parts":[[2021,5,1]],"date-time":"2021-05-01T00:00:00Z","timestamp":1619827200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,5,1]],"date-time":"2021-05-01T00:00:00Z","timestamp":1619827200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,5,1]],"date-time":"2021-05-01T00:00:00Z","timestamp":1619827200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100004826","name":"Beijing Natural Science Foundation","doi-asserted-by":"publisher","award":["L182032"],"award-info":[{"award-number":["L182032"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Internet Things J."],"published-print":{"date-parts":[[2021,5,1]]},"DOI":"10.1109\/jiot.2020.3040736","type":"journal-article","created":{"date-parts":[[2020,11,26]],"date-time":"2020-11-26T21:19:22Z","timestamp":1606425562000},"page":"7270-7278","source":"Crossref","is-referenced-by-count":9,"title":["Consensus Forecast of Rainfall Using Hybrid Climate Learning Model"],"prefix":"10.1109","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5803-9333","authenticated-orcid":false,"given":"Neethu","family":"Madhukumar","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8373-535X","authenticated-orcid":false,"given":"Eric","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9182-6267","authenticated-orcid":false,"given":"Yi-Fan","family":"Zhang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0608-065X","authenticated-orcid":false,"given":"Wei","family":"Xiang","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2019.2913176"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2018.2878477"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2839699"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/S0895-7177(00)00272-7"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2946057"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1175\/1520-0469(1980)037<0545:EOIAFO>2.0.CO;2"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2017.2764116"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/s00376-012-1259-9"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2880044"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2017.7952603"},{"key":"ref28","first-page":"281","article-title":"Random search for hyper-parameter optimization","volume":"13","author":"bergstra","year":"2012","journal-title":"J Mach Learn Res"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2014.2315771"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/ASRU.2013.6707742"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/s00382-017-3974-5"},{"key":"ref6","first-page":"84","article-title":"A survey on rainfall prediction using data mining","volume":"2","author":"sangari","year":"2014","journal-title":"Int J Comput Sci Mobile Appl"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2018.2877510"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.22499\/3.6703.001"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TASLP.2019.2940662"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2004.842338"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2016.2585575"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2017.2677578"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2018.2812155"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhydrol.2009.06.047"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhydrol.2013.08.035"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.3233\/IDA-173427"},{"key":"ref24","year":"2015","journal-title":"Meteorological Verification Data Technical"},{"key":"ref23","first-page":"5622","article-title":"Deep learning for precipitation nowcasting: A benchmark and a new model","author":"shi","year":"2017","journal-title":"Advances in neural information processing systems"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2011.2128342"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2009.2032543"}],"container-title":["IEEE Internet of Things Journal"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6488907\/9411778\/09272620.pdf?arnumber=9272620","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T14:53:43Z","timestamp":1652194423000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9272620\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,1]]},"references-count":29,"journal-issue":{"issue":"9"},"URL":"https:\/\/doi.org\/10.1109\/jiot.2020.3040736","relation":{},"ISSN":["2327-4662","2372-2541"],"issn-type":[{"value":"2327-4662","type":"electronic"},{"value":"2372-2541","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,1]]}}}