Work

Quantifying the Lagged Teleconnection Between the Southern Oscillation Index (SOI) and the Bushfire Danger Index

Year: 2025

Type: article

Abstract: To improve preparedness and minimise losses, this paper presents the development of artificial intelligence (AI)-based forecasting models using a large-scale climate index for Victoria (Australia), which is known to be one of the most fire-prone areas in the country. Using an Artificial Neural Network (ANN) approach, this study investigates the nonlinear relationships between SOI and the Forest Fire Danger Index (FFDI) to develop a robust predictive model. Levenberg–Marquardt optimisation through the backpropagation method was employed to train the ANN models. Based on local climate data, FFDI values were calculated for eight locations within southeast Australia, and SOI values of earlier months were correlated with the FFDI values of the later months. A total of 55 years (1965–2019) of monthly SOI and FFDI values were used to train, validate, and test the developed ANN models. The findings show that the developed models can predict future FFDI values, having correlation coefficients ranging 0.71~0.96, 0.70~0.95, and 0.75~0.93 for 1-month, 2-month, and 3-month lagged periods, respectively. As is obvious, one-month-ahead predictions were more accurate than two/three-month-ahead predictions. In general, the stations located in the eastern parts are attributed to higher prediction accuracy than stations located in the western regions, possibly due to their closer proximity to the location from where SOI originates (i.e., southern Pacific). These variations between the stations located in the eastern and western parts may partly exhibit the applicability of FFDI to different vegetation types. However, the outcomes hold potential for informing stakeholders, improving resource allocation for fire preparedness, and mitigating the devastating impacts of bushfires on communities and ecosystems.

Source: Fire

DOI: 10.3390/fire8110444

Authors: Monzur Alam Imteaz, Afsin Islam, Iqbal Hossain, Md Jahangir Alam

Institutions: Swinburne University of Technology, Murray Darling Basin Authority

Language: English

Topic: Fire effects on ecosystems

Subfield: Global and Planetary Change

Field: Environmental Science

Domain: Physical Sciences

SDGs: Life on land, Climate action

Open Access status: gold

Estimated APC paid: $2,896