Dutta, RDas, AAryal, J2017-11-142016-02-102016-022016-01-122054-5703EK4GN (isidoc)26998312 (pubmed)https://hdl.handle.net/10182/8746Increasing Australian bush-fire frequencies over the last decade has indicated a major climatic change in coming future. Understanding such climatic change for Australian bush-fire is limited and there is an urgent need of scientific research, which is capable enough to contribute to Australian society. Frequency of bush-fire carries information on spatial, temporal and climatic aspects of bush-fire events and provides contextual information to model various climate data for accurately predicting future bush-fire hot spots. In this study, we develop an ensemble method based on a two-layered machine learning model to establish relationship between fire incidence and climatic data. In a 336 week data trial, we demonstrate that the model provides highly accurate bush-fire incidence hot-spot estimation (91% global accuracy) from the weekly climatic surfaces. Our analysis also indicates that Australian weekly bush-fire frequencies increased by 40% over the last 5 years, particularly during summer months, implicating a serious climatic shift.11 pagesElectronic-eCollectionen© 2016 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.bush-fire frequencyensemble machine learningbig dataclimatic shiftdecision scienceJournal Article10.1098/rsos.150241ANZSRC::04 Earth SciencesANZSRC::05 Environmental SciencesANZSRC::070503 Forestry Fire ManagementANZSRC::040104 Climate Change Processes2054-5703https://creativecommons.org/licenses/by/4.0/Attribution