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An Empirical Analysis of the wildfire ignitions in Australia using machine learning techniques
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  • David Tania,
  • Ishaani Priyadarshini,
  • Sandipan Sahu,
  • Raghvendra Kumar,
  • Shiyang Lyu
David Tania
Monash University Faculty of Information Technology

Corresponding Author:[email protected]

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Ishaani Priyadarshini
University of California Berkeley School of Information
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Sandipan Sahu
Gandhi Institute of Engineering and Technology
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Raghvendra Kumar
Gandhi Institute of Engineering and Technology
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Shiyang Lyu
Monash University Faculty of Information Technology
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Abstract

Over the last few decades, there has been an increase in the probability of occurrence of wildfires. Also known as bushfires, the catastrophe can be attributed to climate changes and extreme weather conditions. Australia’s dry and warm climate makes it prone to wildfires, which risks the ecosystem and decreases the forest area. Hence it is necessary to reduce bushfire risk by monitoring their intensity. The availability of remotely sensed data enables us to analyse wildfires, explore and discover patterns, and help provide real-time warnings. This paper examines the forest fire data from 2018-2020, considering parameters like Brightness (Prediction) and Fire Radiative Power (Classification). The analysis is conducted using several machine learning algorithms like Random Forest (RF), Decision Trees (DT), K-Nearest Neighbors (kNN), eXtreme Gradient Boosting (xGB), Artificial Neural Networks (ANN), Convolution Neural Networks (CNN), etc. The prediction models are evaluated using Mean squared error (MSE), Root Mean Square Error (RMSE), R squared (R2), and Mean Absolute Error (MAE). In contrast, the classification models are evaluated using accuracy, precision, recall, and F-1 score. Our study shows that the RF model is the best prediction model, and the ANN model is the best classification model compared to the baseline models.