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 …
Artificial neural network approaches for disaster management: A literature review
Disaster management (DM) is one of the leading fields that deal with the humanitarian
aspects of emergencies. The field has attracted researchers because of its ever-increasing …
aspects of emergencies. The field has attracted researchers because of its ever-increasing …
Machine learning based wildfire susceptibility mapping using remotely sensed fire data and GIS: A case study of Adana and Mersin provinces, Turkey
MC Iban, A Sekertekin - Ecological Informatics, 2022 - Elsevier
In recent years, the number of wildfires has increased all over the world. Therefore, mapping
wildfire susceptibility is crucial for prevention, early detection, and supporting wildfire …
wildfire susceptibility is crucial for prevention, early detection, and supporting wildfire …
Application of machine learning models in the behavioral study of forest fires in the Brazilian Federal District region
JNS Rubí, PHP de Carvalho, PRL Gondim - Engineering Applications of …, 2023 - Elsevier
Ecosystems, settlements, and human lives are put at risk by forest fires every year. Several
models proposed for the prediction of their occurrence and behavior have aimed at …
models proposed for the prediction of their occurrence and behavior have aimed at …
Estimating the probability of wildfire occurrence in Mediterranean landscapes using Artificial Neural Networks
Wildfires are a major disturbance in the Mediterranean Basin and an ecological factor that
constantly alters the landscape. In this context, it is crucial to understand where wildfires are …
constantly alters the landscape. In this context, it is crucial to understand where wildfires are …
Spatial assessment of PM10 hotspots using random forest, K-nearest neighbour and Naïve Bayes
Spatial modelling and analysis can assist in improving the decision-making process of
mitigating bad air quality. One of Malaysia's most harmful air pollutants is particulate matter …
mitigating bad air quality. One of Malaysia's most harmful air pollutants is particulate matter …
Assessing the predictive efficacy of six machine learning algorithms for the susceptibility of Indian forests to fire
Increasing numbers and intensity of forest fires indicate that forests have become
susceptible to fires in the tropics. We assessed the susceptibility of forests to fire in India by …
susceptible to fires in the tropics. We assessed the susceptibility of forests to fire in India by …
Predicting wildfire burns from big geodata using deep learning
Wildfire continues to be a major environmental problem in the world. To help land and fire
management agencies manage and mitigate wildfire-related risks, we need to develop tools …
management agencies manage and mitigate wildfire-related risks, we need to develop tools …
Enhancing predictive ability of optimized group method of data handling (GMDH) method for wildfire susceptibility mapping
Wildfire is one of the most significant environmental challenges and causing damage to
ecosystems, habitats, infrastructure and especially human lives. Thus, spatial assessment of …
ecosystems, habitats, infrastructure and especially human lives. Thus, spatial assessment of …
Modeling susceptibility to forest fires in the Central Corridor of the Atlantic Forest using the frequency ratio method
RO de Santana, RC Delgado, A Schiavetti - Journal of environmental …, 2021 - Elsevier
Fire is one of the main disturbances of tropical forests. Understanding the spatial and
temporal dynamics of forest fires is of fundamental importance for the conservation of …
temporal dynamics of forest fires is of fundamental importance for the conservation of …