Wildfire hazard prediction: a fuzzy model for Sensor Embedded Intelligence
Koppula, Lakshmi Bhargavi
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This thesis investigates the topic of “Wildfire hazard prediction” through conducting an in-depth study on fuzzy prediction methods and geographically collected weather data. The study explores the impact of various environmental factors leading to Wildfire. These factors associated with Wildfire are extracted from analyzing the past raw weather data and using McArthur’s Fire Danger Index formulations. The indices calculated through the formula and the generated synthetic data are used to train a Fuzzy system developed in Matlab software. The trained Fuzzy system is then tested with a raw set of historical real weather data originated from National Rural Fire Authority (NRFA) and National Institute of Water and Atmospheric Research (NIWA) to analyze the accuracy of the system developed. Finally, the predicted results of the Fuzzy system are examined and compared with that calculated using the formula, including the error percentiles between the two. Impacts of the input weather factors are also plotted in relation to the Fire Danger Indices under various conditions to understand their sensitivity towards the final prediction.