Emulation of Forest Fire Spread Using LSTM and Cellular Automata

Forests are one of the most valuable and necessary resources to protect the ecological balance of the planet, a natural habitat for animals, and they are vital to our lives in many direct and indirect ways. However, forest fires can cause severe damage to the land and many other resources such as property, human life, and wildlife. Previously, metacellular automata have been used to simulate the spread of forest fires, and the traditional metacellular automata model outputs the spread of forest fires by inputting forest fire-related factors such as topography, meteorology, and vegetation type, but these inputs are highly unstable and fires can have an effect on the wind direction and speed, so the inputs must be calibrated. The aim of this study is to explore a forest fire spread simulation model based on Long Short-Term Memory(LSTM) and Cellular Automaton (CA), and to improve the accuracy of the simulation results by calibrating the incoming parameters of the CA in order to improve the fire-fighting action during forest fires.