Forest Fire Detection using CNN-RF and CNN-XGBOOST Machine Learning Algorithms
Abstract
Detection of forest fire should be quick and accurate
as forests are the important sources to lead a vital life
on earth. Detection of fire can be extremely difficult
using existing methods of smoke sensors installed
and they are slow and cost inefficient, so in order to
avoid large scale fires, detection from visual scenes is
required. In this work detection of fire in an image is
done by extracting features using Deep learning
algorithm and with those features as input to machine
learning algorithm, a model is build with the help of
different machine learning algorithms like Random
Forest, Support Vector Machine, XGBoost and KMeans
Clustering. Using these algorithms the data
sets are classified into fire and non fire images to
build the model and the test data of the data set is
provided as input for getting the validation accuracy
of the model. Then comparison is done among
machine learning algorithms to find which algorithm
provides more accuracy. To test the accuracy of the
fire presence classification evaluation metrics are
used in the model and find that accuracy of CNN-RF
and CNN-XGBOOST are 98.53% which is greater
than accuracy of CNN-SVM 97.06%..
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References
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