A0215
Title: Modelling and prediction of the wildfire data using a fractional Poisson process
Authors: Sudeep Bapat - Indian Institute of Technology Bombay (India) [presenting]
Abstract: Modelling wildfire events has been studied in the literature using the Poisson process, which essentially assumes the independence of wildfire events. The fractional Poisson process is used to model the wildfire occurrences in California between June 2019 and April 2023 and predict the wildfire events that explain the underlying memory between these events. The method of moments and maximum likelihood estimate introduced approaches to estimate the parameters of the fractional Poisson process, which is an alternative to the method proposed in a prior study. The estimates of the fractional parameter are obtained as 0.8, proving that the wildfire events are dependent. The proposed model has reduced prediction error by 90\% compared to the classical Poisson process model.
