Modelling wildfire events has been studied in the literature using the Poisson process, which essentially assumes the independence of wildfire events. In this paper, we use the fractional Poisson process to model the wildfire occurrences in California between June 2019 - April 2023 and predict the wildfire events that explains the underlying memory between these events. We introduce method of moments and maximum likelihood estimate approaches to estimate the parameters of the fractional Poisson process, which is an alternative to the method proposed by Cahoy (2010). We obtain the estimates of the fractional parameter 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.

翻译：现有文献中通常采用泊松过程对野火事件进行建模，该方法本质上假设野火事件相互独立。本文采用分数泊松过程对2019年6月至2023年4月期间加利福尼亚州的野火发生情况进行建模，并基于该模型预测能够解释事件间内在记忆性的野火事件。我们提出了矩估计法和最大似然估计法来估计分数泊松过程的参数，这是对Cahoy（2010）所提方法的替代方案。分数参数的估计值为0.8，证明野火事件之间存在依赖性。与经典泊松过程模型相比，所提模型的预测误差降低了90%。