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Predicting the burned area in forest using Machine learning techniques

Ramasubramanian, Srinivas (2017) Predicting the burned area in forest using Machine learning techniques. Masters thesis, Dublin, National College of Ireland.

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Abstract

Predicting the amount of land burnt during forest fires is one of the challenging tasks. Forest fire causes serious damage to the ora and fauna of a country. This is one of major environmental issues which can also affect the economy of a country. Early prediction of fires saves large number of flora and fauna and prevents the ecosystem. By predicting the area burnt we can also classify whether the fire into small or big. The key motivation for this prediction is to help fire management team in proper resource allocation and to help the fire fighters in a best possible way. The meteorological conditions of the forest are the key factors of the forest fire. These climatic data can be obtained using the local sensors which are incorporated in the nearest meteorological stations. This research proposes various Machine learning approaches such as Linear regression, logistic regression, SVR, Random forest, Gradient boosting and Bagging for predicting the amount of land burnt in the forest. Here the predictive model is build using the outbreaks of fire caused in the northeast region of Portugal.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science

G Geography. Anthropology. Recreation > GE Environmental Sciences > Environment
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: CAOIMHE NI MHAICIN
Date Deposited: 30 Aug 2018 12:29
Last Modified: 30 Aug 2018 12:29
URI: http://trap.ncirl.ie/id/eprint/3103

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