In this research, we perfom analysis on wildfire dataset, and propose a model to classify the wildfires based on environmental and geographical data.
Full documentation is available inside "/docs" directory.
Environmental, social, and economic causatums from wildfires have been continuously increasing around the world over the past decade. These fires not only devastated forest and grassland but also detrimentally impacted wildfire habitat, water quality & supply, tourism, and property values. In the past few years, a number of research studies have been conducted to monitor, predict and prevent wildfires using several Artificial Intelligence techniques such as Machine Learning, Deep Learning, Big data, and Remote Sensing. In this paper, we proposed the wildfire classification and prediction system to classify the wildfires into elven different types based on the data on temperature anomalies from satellites and geographical data using the CatBoost classifier. Quality metric - multi-class ROC-AUC has been considered to evaluate the performance of the system. The proposed system achieved high performance on the test set.
We collect the environmental data from National Centers for Environmental Prediction (NCEP)
Source: NCEP data
In this project, we analysed the wildfire data and proposed a model to classify the wildfires based on ensemble learning.