eprintid: 1544034 rev_number: 31 eprint_status: archive userid: 608 dir: disk0/01/54/40/34 datestamp: 2017-03-07 12:11:57 lastmod: 2021-10-18 22:27:48 status_changed: 2017-03-07 12:12:36 type: proceedings_section metadata_visibility: show creators_name: Shi, Y creators_name: Taalab, K creators_name: Cheng, T title: Flood Prediction Using Support Vector Machines (SVM) ispublished: pub divisions: UCL divisions: B04 divisions: C05 divisions: F44 keywords: Flooding, Support Vector Machines, River Flow, Rainfall abstract: Flooding is a destructive phenomenon that can risk human life, damage homes and have huge economic impacts. To plan and implement effective mitigation strategies, it is necessary to predict when and where flooding will occur. Based on a combination of rain gauge and river discharge measurement taken from the River Don catchment, UK this study proposes a Support Vector Machine (SVM) based approach to predicting river. The purpose of this work is to show the potential of the SVM method for predicting future flood events. date: 2016-04-01 date_type: published publisher: GIS Research UK (GISRUK) official_url: http://www.gre.ac.uk/ach/services/events/gisruk2016/home oa_status: green full_text_type: other language: eng primo: open primo_central: open_green verified: verified_manual elements_id: 1212458 lyricists_name: Cheng, Tao lyricists_id: TCHEN23 actors_name: Ndieyira, Joseph actors_name: Laslett, David actors_id: JWNDI09 actors_id: DLASL34 actors_role: owner actors_role: impersonator full_text_status: public series: GIS Research UK (GISRUK) Conference volume: 24 place_of_pub: London, UK event_title: The 24th GIS Research UK (GISRUK) Conference (GISRUK2016) event_location: London, UK event_dates: 30 March 2017 - 01 April 2017 institution: GISRUK2016 book_title: Proceedings of the 24th GIS Research UK (GISRUK) Conference citation: Shi, Y; Taalab, K; Cheng, T; (2016) Flood Prediction Using Support Vector Machines (SVM). In: Proceedings of the 24th GIS Research UK (GISRUK) Conference. GIS Research UK (GISRUK): London, UK. Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/1544034/1/Cheng_GISRUK_2016_paper_18%20Yu%20Shi.pdf