A review of machine learning applications in wildfire science and management

P Jain, SCP Coogan, SG Subramanian… - Environmental …, 2020‏ - cdnsciencepub.com
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 …

[HTML][HTML] A brief review of machine learning algorithms in forest fires science

R Alkhatib, W Sahwan, A Alkhatieb, B Schütt - Applied Sciences, 2023‏ - mdpi.com
Due to the harm forest fires cause to the environment and the economy as they occur more
frequently around the world, early fire prediction and detection are necessary. To anticipate …

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 …

[HTML][HTML] Performance evaluation of machine learning methods for forest fire modeling and prediction

BT Pham, A Jaafari, M Avand, N Al-Ansari, T Dinh Du… - Symmetry, 2020‏ - mdpi.com
Predicting and map** fire susceptibility is a top research priority in fire-prone forests
worldwide. This study evaluates the abilities of the Bayes Network (BN), Naïve Bayes (NB) …

[HTML][HTML] Leveraging the power of internet of things and artificial intelligence in forest fire prevention, detection, and restoration: A comprehensive survey

S Giannakidou, P Radoglou-Grammatikis, T Lagkas… - Internet of Things, 2024‏ - Elsevier
Forest fires are a persistent global problem, causing devastating consequences such as loss
of human lives, harm to the environment, and substantial economic losses. To mitigate these …

A survey of machine learning algorithms based forest fires prediction and detection systems

F Abid - Fire technology, 2021‏ - Springer
Forest fires are one of the major environmental concerns, each year millions of hectares are
destroyed over the world, causing economic and ecological damage as well as human lives …

A hybrid artificial intelligence approach using GIS-based neural-fuzzy inference system and particle swarm optimization for forest fire susceptibility modeling at a …

DT Bui, QT Bui, QP Nguyen, B Pradhan… - Agricultural and forest …, 2017‏ - Elsevier
This paper proposes and validates a novel hybrid artificial intelligent approach, named as
Particle Swarm Optimized Neural Fuzzy (PSO-NF), for spatial modeling of tropical forest fire …

Testing a new ensemble model based on SVM and random forest in forest fire susceptibility assessment and its map** in Serbia's Tara National Park

L Gigović, HR Pourghasemi, S Drobnjak, S Bai - Forests, 2019‏ - mdpi.com
The main objectives of this paper are to demonstrate the results of an ensemble learning
method based on prediction results of support vector machine and random forest methods …

Towards an integrated approach to wildfire risk assessment: when, where, what and how may the landscapes burn

E Chuvieco, M Yebra, S Martino, K Thonicke… - Fire, 2023‏ - mdpi.com
This paper presents a review of concepts related to wildfire risk assessment, including the
determination of fire ignition and propagation (fire danger), the extent to which fire may …

Modeling spatial patterns of fire occurrence in Mediterranean Europe using Multiple Regression and Random Forest

S Oliveira, F Oehler, J San-Miguel-Ayanz… - Forest Ecology and …, 2012‏ - Elsevier
Fire occurrence, which results from the presence of an ignition source and the conditions for
a fire to spread, is an essential component of fire risk assessment. In this paper, we present …