{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T17:45:54Z","timestamp":1755798354552,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":27,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,8,14]],"date-time":"2021-08-14T00:00:00Z","timestamp":1628899200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["2006633"],"award-info":[{"award-number":["2006633"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,8,14]]},"DOI":"10.1145\/3447548.3467400","type":"proceedings-article","created":{"date-parts":[[2021,8,12]],"date-time":"2021-08-12T06:12:03Z","timestamp":1628748723000},"page":"1677-1685","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["JOHAN"],"prefix":"10.1145","author":[{"given":"Ding","family":"Wang","sequence":"first","affiliation":[{"name":"Michigan State University, East Lansing, MI, USA"}]},{"given":"Pang-Ning","family":"Tan","sequence":"additional","affiliation":[{"name":"Michigan State University, East Lansing, MI, USA"}]}],"member":"320","published-online":{"date-parts":[[2021,8,14]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301468"},{"volume-title":"Efficient learning machines","author":"Awad Mariette","key":"e_1_3_2_2_2_1","unstructured":"Mariette Awad and Rahul Khanna . 2015. Support vector regression . In Efficient learning machines . Springer , 67--80. Mariette Awad and Rahul Khanna. 2015. Support vector regression. In Efficient learning machines. Springer, 67--80."},{"key":"e_1_3_2_2_3_1","volume-title":"Tropical Cyclone Report: Hurricane Harvey","author":"Blake Eric S","year":"2018","unstructured":"Eric S Blake and David A Zelinsky . 2018. Tropical Cyclone Report: Hurricane Harvey . National Hurricane Center ( 2018 ). Eric S Blake and David A Zelinsky. 2018. Tropical Cyclone Report: Hurricane Harvey. National Hurricane Center (2018)."},{"key":"e_1_3_2_2_4_1","volume-title":"Tropical Cyclone Intensity Forecasting: Still a Challenging Proposition","author":"Brown D","year":"2017","unstructured":"D Brown . 2017. Tropical Cyclone Intensity Forecasting: Still a Challenging Proposition . National Hurricane Center ( 2017 ). D Brown. 2017. Tropical Cyclone Intensity Forecasting: Still a Challenging Proposition. National Hurricane Center (2017)."},{"key":"e_1_3_2_2_5_1","volume-title":"Support vector ordinal regression. Neural computation","author":"Chu Wei","year":"2007","unstructured":"Wei Chu and S Sathiya Keerthi . 2007. Support vector ordinal regression. Neural computation , Vol. 19 , 3 ( 2007 ), 792--815. Wei Chu and S Sathiya Keerthi. 2007. Support vector ordinal regression. Neural computation, Vol. 19, 3 (2007), 792--815."},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/COMPSAC.2018.10290"},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.5555\/1248547.1248566"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1175\/1520-0434(1994)009<0209:ASHIPS>2.0.CO;2"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1175\/WAF862.1"},{"key":"e_1_3_2_2_10_1","volume-title":"Support vector regression machines. Advances in neural information processing systems","author":"Drucker Harris","year":"1996","unstructured":"Harris Drucker , Christopher J Burges , Linda Kaufman , Alex Smola , and Vladimir Vapnik . 1996. Support vector regression machines. Advances in neural information processing systems , Vol. 9 ( 1996 ), 155--161. Harris Drucker, Christopher J Burges, Linda Kaufman, Alex Smola, and Vladimir Vapnik. 1996. Support vector regression machines. Advances in neural information processing systems, Vol. 9 (1996), 155--161."},{"key":"e_1_3_2_2_11_1","volume-title":"2019 ESIP Winter Meeting.","author":"Eslami Ebrahim","year":"2019","unstructured":"Ebrahim Eslami , Yunsoo Choi , Yannic Lops , and Alqamah Sayeed . 2019 . A Deep Learning Driven Improved Ensemble Approach for Hurricane Forecasting . In 2019 ESIP Winter Meeting. Ebrahim Eslami, Yunsoo Choi, Yannic Lops, and Alqamah Sayeed. 2019. A Deep Learning Driven Improved Ensemble Approach for Hurricane Forecasting. In 2019 ESIP Winter Meeting."},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13131-018-1219-z"},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.3389\/fdata.2020.00001"},{"key":"e_1_3_2_2_14_1","first-page":"7655","article-title":"Hurricane Weather Research and Forecasting (HWRF) model scientific documentation","volume":"75","author":"Gopalakrishnan SG","year":"2010","unstructured":"SG Gopalakrishnan , Qingfu Liu , Timothy Marchok , Dmitry Sheinin , Naomi Surgi , Robert Tuleya , Richard Yablonsky , and Xuejin Zhang . 2010 . Hurricane Weather Research and Forecasting (HWRF) model scientific documentation . L Bernardet Ed , Vol. 75 (2010), 7655 . SG Gopalakrishnan, Qingfu Liu, Timothy Marchok, Dmitry Sheinin, Naomi Surgi, Robert Tuleya, Richard Yablonsky, and Xuejin Zhang. 2010. Hurricane Weather Research and Forecasting (HWRF) model scientific documentation. L Bernardet Ed, Vol. 75 (2010), 7655.","journal-title":"L Bernardet Ed"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.5670\/oceanog.2014.73"},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2019.00192"},{"key":"e_1_3_2_2_17_1","volume-title":"Proceedings of the Genetic and Evolutionary Computation Conference","author":"Kordmahalleh Mina Moradi","year":"2016","unstructured":"Mina Moradi Kordmahalleh , Mohammad Gorji Sefidmazgi , and Abdollah Homaifar . 2016 . A Sparse Recurrent Neural Network for Trajectory Prediction of Atlantic Hurricanes . In Proceedings of the Genetic and Evolutionary Computation Conference 2016. ACM, 957--964. Mina Moradi Kordmahalleh, Mohammad Gorji Sefidmazgi, and Abdollah Homaifar. 2016. A Sparse Recurrent Neural Network for Trajectory Prediction of Atlantic Hurricanes. In Proceedings of the Genetic and Evolutionary Computation Conference 2016. ACM, 957--964."},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/72.846739"},{"key":"e_1_3_2_2_19_1","volume-title":"Deep Learning for Physical Sciences (DLPS) Workshop, held with NIPS Conference.","author":"Mudigonda Mayur","year":"2017","unstructured":"Mayur Mudigonda , Sookyung Kim , Ankur Mahesh , Samira Kahou , Karthik Kashinath , Dean Williams , Vincen Michalski , Travis O'Brien , and Mr Prabhat . 2017 . Segmenting and tracking extreme climate events using neural networks . In Deep Learning for Physical Sciences (DLPS) Workshop, held with NIPS Conference. Mayur Mudigonda, Sookyung Kim, Ankur Mahesh, Samira Kahou, Karthik Kashinath, Dean Williams, Vincen Michalski, Travis O'Brien, and Mr Prabhat. 2017. Segmenting and tracking extreme climate events using neural networks. In Deep Learning for Physical Sciences (DLPS) Workshop, held with NIPS Conference."},{"key":"e_1_3_2_2_20_1","volume-title":"Assessing the U.S. Climate","author":"NCEI.","year":"2018","unstructured":"NCEI. 2019. Assessing the U.S. Climate in 2018 . National Centers for Environmental Information (NCEI) . NCEI. 2019. Assessing the U.S. Climate in 2018. National Centers for Environmental Information (NCEI)."},{"key":"e_1_3_2_2_21_1","volume-title":"Tropical Cyclones Tables Updated","author":"NHC.","year":"2018","unstructured":"NHC. 2018. Costliest U.S. Tropical Cyclones Tables Updated . National Hurricane Center ( 2018 ). NHC. 2018. Costliest U.S. Tropical Cyclones Tables Updated. National Hurricane Center (2018)."},{"key":"e_1_3_2_2_22_1","volume-title":"Prediction of a typhoon track using a generative adversarial network and satellite images. Scientific reports","author":"R\u00fcttgers Mario","year":"2019","unstructured":"Mario R\u00fcttgers , Sangseung Lee , Soohwan Jeon , and Donghyun You . 2019. Prediction of a typhoon track using a generative adversarial network and satellite images. Scientific reports , Vol. 9 , 1 ( 2019 ), 1--15. Mario R\u00fcttgers, Sangseung Lee, Soohwan Jeon, and Donghyun You. 2019. Prediction of a typhoon track using a generative adversarial network and satellite images. Scientific reports, Vol. 9, 1 (2019), 1--15."},{"key":"e_1_3_2_2_23_1","volume-title":"Tropical Cyclone Report: Hurricane Florence","author":"Stewart Stacy","year":"2019","unstructured":"Stacy Stewart and Robbie Berg . 2019. Tropical Cyclone Report: Hurricane Florence . National Hurricane Center ( 2019 ). Stacy Stewart and Robbie Berg. 2019. Tropical Cyclone Report: Hurricane Florence. National Hurricane Center (2019)."},{"key":"e_1_3_2_2_24_1","volume-title":"DC, USA","author":"Taylor Harvey Thurm","year":"2010","unstructured":"Harvey Thurm Taylor , Bill Ward , Mark Willis , and Walt Zaleski . 2010. The Saffir-Simpson hurricane wind scale. Atmospheric Administration: Washington , DC, USA ( 2010 ). Harvey Thurm Taylor, Bill Ward, Mark Willis, and Walt Zaleski. 2010. The Saffir-Simpson hurricane wind scale. Atmospheric Administration: Washington, DC, USA (2010)."},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5444"},{"key":"e_1_3_2_2_26_1","volume-title":"Science","volume":"371","author":"Wang Shuai","year":"2021","unstructured":"Shuai Wang and Ralf Toumi . 2021 . Recent migration of tropical cyclones toward coasts . Science , Vol. 371 , 6528 (2021), 514--517. Shuai Wang and Ralf Toumi. 2021. Recent migration of tropical cyclones toward coasts. Science, Vol. 371, 6528 (2021), 514--517."},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2014.90"}],"event":{"name":"KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"],"location":"Virtual Event Singapore","acronym":"KDD '21"},"container-title":["Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &amp; Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3447548.3467400","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3447548.3467400","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3447548.3467400","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:18:36Z","timestamp":1750191516000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3447548.3467400"}},"subtitle":["A Joint Online Hurricane Trajectory and Intensity Forecasting Framework"],"short-title":[],"issued":{"date-parts":[[2021,8,14]]},"references-count":27,"alternative-id":["10.1145\/3447548.3467400","10.1145\/3447548"],"URL":"https:\/\/doi.org\/10.1145\/3447548.3467400","relation":{},"subject":[],"published":{"date-parts":[[2021,8,14]]},"assertion":[{"value":"2021-08-14","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}