Estimating fire Duration using regression methods
Hansong Xiao
Wildfire forecasting problems usually rely on complex grid-based mathematical
models, mostly involving Computational fluid dynamics(CFD) and Celluar
Automata, but these methods have always been computationally expensive and
difficult to deliver a fast decision pattern. In this paper, we provide machine
learning based approaches that solve the problem of high computational effort
and time consumption. This paper predicts the burning duration of a known
wildfire by RF(random forest), KNN, and XGBoost regression models and also
image-based, like CNN and Encoder. Model inputs are based on the map of
landscape features provided by satellites and the corresponding historical fire
data in this area. This model is trained by happened fire data and landform
feature maps and tested with the most recent real value in the same area. By
processing the input differently to obtain the optimal outcome, the system is
able to make fast and relatively accurate future predictions based on landscape
images of known fires.