Modeling Conditions Appropriate for Wildfire in South East China – A Machine Learning Approach

Wildfire is one of the most common natural hazards in the world.It has tendency to destroy large fields of land and make them uncultivable effecting agriculture and hence economy of the region. China is one of the countries that have a serious wildfire problem and is among the top 20 countries most affected in terms of economic loss as a result of wildfire. Fire risk estimation for purposes of risk reduction is an important aspect in disaster studies around the world. Therefore, it is highly pertinent to estimate fire risk in the region as it will empower research to formulate plans and methodology for short and long terms risk reduction.The aim of this research was to develop a model that uses a binary classification system to identify likely fire grids in an area of interest. The variations in different environmental and climatic parameters leading up to fire events during a period of 9 years(2003-2011)were modeled. Three natural vegetation land covers dominate the study area of South East China, namely Evergreen Broadleaf Forest, Mixed Forest and Woody Savannas. Different models were trained for each of these land covers. The model for Mixed Forest land cover performed the best compared to the models for Evergreen Broadleaf Forest and Woody Savannas. It was found that better representation of Mixed Forest in training samples made this model perform more reliably as compared to other. Improving the individual models constructed for different land covers and combining them can provide fire classification for a larger region. There is room to improve the spatial precision of fire grid classification. Introducing finer scale features that have higher correlation with fire activity and exhibit high spatial variability seems viable way forward.