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 …

Human-caused fire occurrence modelling in perspective: a review

S Costafreda-Aumedes, C Comas… - International Journal of …, 2017 - CSIRO Publishing
The increasing global concern about wildfires, mostly caused by people, has triggered the
development of human-caused fire occurrence models in many countries. The premise is …

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 …

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 …

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) …

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 …

A novel ensemble modeling approach for the spatial prediction of tropical forest fire susceptibility using LogitBoost machine learning classifier and multi-source …

MS Tehrany, S Jones, F Shabani… - Theoretical and Applied …, 2019 - Springer
A reliable forest fire susceptibility map is a necessity for disaster management and a primary
reference source in land use planning. We set out to evaluate the use of the LogitBoost …

Investigation of general indicators influencing on forest fire and its susceptibility modeling using different data mining techniques

ZS Pourtaghi, HR Pourghasemi, R Aretano… - Ecological …, 2016 - Elsevier
Forests are living dynamic systems and these unique ecosystems are essential for life on
earth. Forest fires are one of the major environmental concerns, economic, and social in the …

Spatio-temporal analysis of forest fire events in the Margalla Hills, Islamabad, Pakistan using socio-economic and environmental variable data with machine learning …

A Tariq, H Shu, S Siddiqui, I Munir, A Sharifi… - Journal of Forestry …, 2022 - Springer
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 …

A machine learning framework for multi-hazards modeling and map** in a mountainous area

S Yousefi, HR Pourghasemi, SN Emami, S Pouyan… - Scientific Reports, 2020 - nature.com
This study sought to produce an accurate multi-hazard risk map for a mountainous region of
Iran. The study area is in southwestern Iran. The region has experienced numerous extreme …