Big data integration shows Australian bush-fire frequency is increasing significantly. Issue 2 (February 2016)

• Record Type: Journal Article
• Authors: Dutta, Ritaban; Das, Aruneema; Aryal, Jagannath
• Is Part Of: Royal Society open science. Volume 3:Issue 2(2016)
• Publication Date: 2016-02
• Subjects: bush-fire frequency -- ensemble machine learning -- big data -- climatic shift -- decision science; Science -- Periodicals; 500
• DOI: 10.1098/rsos.150241
• Languages: English
• ISSNs: 2054-5703

Abstract

Abstract : Increasing 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.