An empirical and dynamic tool for prediction of forest fire spread using RS and ML techniques

Australia has a total of 134 million hectares of forest, which is equivalent to 17% of Australia’s land area. Of this total forest area, determined as at 2016, 132 million hectares (98%) are ‘Native forests’, 1.95 million hectares are ‘Commercial plantations’, and 0.47 million hectares are ‘other forest’. Australia has about 3% of the world’s forest area and globally is the country with the seventh-largest forest area. The Australian ecosystem is shaped by fires for over 70 000 years and each ecosystem has its fire regimes. Additionally, fuel management and predicting flammable areas are the key to managing wildfires. These factors play a vital role in resource allocation, mitigation and recovery efforts. Forest fire is a major ecological disaster, which has economic, social and environmental impacts on humans and also causes the loss of biodiversity. Therefore, it is important to know and understand the behaviour of fire ignition and spread so that fire management agencies can prevent and mitigate wildfires. This project aims to develop a tool to predict Forest Fire Spread using Machine Learning approach and Weather Research and Forecasting with fire spread model (WRF-SFIRE).

In this project, fire risk probability mapping, Prediction of fire points, and fire spread modelling will be carried out for the flammable forest areas in Australia. The fire risk probability model will be prepared by using the two-step Analytic Hierarchy Process (AHP) approach. The fire risk probability model will also be used for cross-validation of Support Vector Machine (SVM) model outputs. Then the Prediction of fire points will be done using SVM model (Linear kernel, Polynomial kernel, Radial kernel, sigmoid kernel) taking elevation, slope, aspect, Soil Moisture Layer (SML), Land Surface Temperature (LST), vegetation type layers for training the data set. Then the Weather Research and Forecasting (WRF) model will be used for obtaining meteorological data for the input of fire spread model using the Global Forecast System (GFS) as source data. After that, the fire spread path will be traced for various recent past fire event. The additional variables will be used for spread modelling other than SVM are, canopy cover, wind, temperature. The Cellular Automata (CA) fire spread model will setup in any two different locations of Australia.


Project Leader:
Sanjeev Kumar Srivastava, University of Sunshine Coast

PhD Student:
Harikesh Singh