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WA Branch August Meeting

15 Sep 2020 10:03 AM | Marie-Louise Rankin (Administrator)

The speaker for the August meeting with the Western Branch of the Statistical Society of Australia and the International Biometrics Society Australasian Region was Dr Smaila Sanni. Currently, Dr. Sanni works at SAGI-West as a biometrician, and has been in that role since October 2019. His talk discussed the research that Dr. Sanni conducted with the Applied and Industrial Mathematics group from the University of New South Wales for a chapter in the book 'Advances in Forest Fire Research 2018' (Jovanoski et al., 2018). The work considered methods in stochastic differential equations to describe the rate of spread (ROS) of fire. Interest is in how the fire develops within the initial minutes of a fire starting. The method used in this work can be applied to numerous fields, including cellular dynamics, animal genetics, disease spread in crops, and yield response to changes in growth factors.

The accelerated phase of fire ROS is incredibly important to know as it can inform first responders of how rapidly a fire may develop, and what resources they will therefore need to handle that situation. While undoubtedly an important topic, the majority of studies in wildland fire science have been dedicated to the development of models after its initial acceleration phase, when the fire has reached a quasi- equilibrium rate of spread. Comparatively little attention has been given to the development of models that specifically account for the growth phase of a fire's development.

The findings put forward by Dr. Sanni and his research team presented interesting relationships in fire dynamics. One key finding was that the probability that the fire would self-extinguish was found to be governed by the ratio between the equilibrium fire ROS and the variability of the fire ROS. Another notable outcome was that the confidence interval for the stochastic differential equation model used in the research was found to be narrower than that used in nonlinear regression models. This means that the stochastic model gives a higher level of precision for its predictions than that found for the nonlinear regression model. Finally, the stochastic model is also advantageous as it provides a way to generate statistics such as the mean, variance, containment probability and the distribution of fire ROS.

As mentioned above, the use of stochastic differential equations can be applied to other fields, and Dr. Sanni sees potential for the use of the modelling in areas such as crop disease spread which undoubtedly has stochastic elements. He hopes that this can be integrated into his work done at SAGI-West to improve their current outcomes.

Due to the nature of the online talk, no dinner was planned afterwards but one was offered for Dr. Sanni in the future.

Jordan Brown


Jovanoski, Z., J. J. Sharples, A. M. Gill, S. Watt, H. S. Sidhu, I. N. Towers, and S. Sanni. 2018. "Modelling the rate of spread of fire: An SDE approach." In  Advances in Forest Fire Research 2018 VIEGAS, D. X., 555-565: Imprensa da Universidade de Coimbra.

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