A review of machine learning applications in wildfire science and management
Artificial intelligence has been applied in wildfire science and management since the 1990s,
with early applications including neural networks and expert systems. Since then, the field …
with early applications including neural networks and expert systems. Since then, the field …
[HTML][HTML] A systematic review of applications of machine learning techniques for wildfire management decision support
Wildfires threaten and kill people, destroy urban and rural property, degrade air quality,
ravage forest ecosystems, and contribute to global warming. Wildfire management decision …
ravage forest ecosystems, and contribute to global warming. Wildfire management decision …
[HTML][HTML] Explainable artificial intelligence (XAI) for interpreting the contributing factors feed into the wildfire susceptibility prediction model
A Abdollahi, B Pradhan - Science of the Total Environment, 2023 - Elsevier
One of the worst environmental catastrophes that endanger the Australian community is
wildfire. To lessen potential fire threats, it is helpful to recognize fire occurrence patterns and …
wildfire. To lessen potential fire threats, it is helpful to recognize fire occurrence patterns and …
[HTML][HTML] Forest fire susceptibility modeling using a convolutional neural network for Yunnan province of China
G Zhang, M Wang, K Liu - International Journal of Disaster Risk Science, 2019 - Springer
Forest fires have caused considerable losses to ecologies, societies, and economies
worldwide. To minimize these losses and reduce forest fires, modeling and predicting the …
worldwide. To minimize these losses and reduce forest fires, modeling and predicting the …
[HTML][HTML] Forest fire susceptibility and risk mapping using social/infrastructural vulnerability and environmental variables
Forests fires in northern Iran have always been common, but the number of forest fires has
been growing over the last decade. It is believed, but not proven, that this growth can be …
been growing over the last decade. It is believed, but not proven, that this growth can be …
[HTML][HTML] A Google Earth Engine approach for wildfire susceptibility prediction fusion with remote sensing data of different spatial resolutions
The effects of the spatial resolution of remote sensing (RS) data on wildfire susceptibility
prediction are not fully understood. In this study, we evaluate the effects of coarse (Landsat 8 …
prediction are not fully understood. In this study, we evaluate the effects of coarse (Landsat 8 …
Spatio-temporal analysis of forest fire events in the Margalla Hills, Islamabad, Pakistan using socio-economic and environmental variable data with machine learning …
Most forest fires in the Margalla Hills are related to human activities and socioeconomic
factors are essential to assess their likelihood of occurrence. This study considers both …
factors are essential to assess their likelihood of occurrence. This study considers both …
Forecasting fire risk with machine learning and dynamic information derived from satellite vegetation index time-series
Fire risk mapping–mapping the probability of fire occurrence and spread–is essential for pre-
fire management as well as for efficient firefighting efforts. Most fire risk maps are generated …
fire management as well as for efficient firefighting efforts. Most fire risk maps are generated …
[HTML][HTML] Spatial prediction of wildfire susceptibility using field survey GPS data and machine learning approaches
Recently, global climate change discussions have become more prominent, and forests are
considered as the ecosystems most at risk by the consequences of climate change. Wildfires …
considered as the ecosystems most at risk by the consequences of climate change. Wildfires …
Machine-learning modelling of fire susceptibility in a forest-agriculture mosaic landscape of southern India
The recurrent forest fires have been a serious management concern in southern Western
Ghats, India. This study investigates the applicability of various geospatial data, machine …
Ghats, India. This study investigates the applicability of various geospatial data, machine …