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California Wildfire Spread Prediction using FARSITE and the Comparison with the Actual Wildfire Maps using Statistical Methods


The unpredictability of wildfires has always been a major problem that brings a vast amount of devastation to the environment and human lives every year. This project uses the program R, QGIS 3 and FARSITE version 3. I implemented the simulations in FARSITE on 10 separate wildfire datasets in California. The datasets include canopy, fuels, weather, perimeters and geographic setting. Map projection was transformed from WGS84 to Albers. Predictions of wildfire maps are generated from FARSITE models in terms of vector data, raster data, and shapefiles. Statistical methods were applied to measure the similarity between the predictive wildfires area and the actual wildfire areas. The methods include Sorensen's Q statistic, Jaccard similarity coefficient, and Hamming distance. Area of intersection and union were calculated. The results of these statistics show that the performance of FARSITE simulation model is acceptable, which can be an option for predicting future wildfires.

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