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[HTML][HTML] A brief review of machine learning algorithms in forest fires science
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 …
frequently around the world, early fire prediction and detection are necessary. To anticipate …
[HTML][HTML] The role of remote sensing for the assessment and monitoring of forest health: A systematic evidence synthesis
Forests are increasingly subject to a number of disturbances that can adversely influence
their health. Remote sensing offers an efficient alternative for assessing and monitoring …
their health. Remote sensing offers an efficient alternative for assessing and monitoring …
Machine learning based wildfire susceptibility map** using remotely sensed fire data and GIS: A case study of Adana and Mersin provinces, Turkey
In recent years, the number of wildfires has increased all over the world. Therefore, map**
wildfire susceptibility is crucial for prevention, early detection, and supporting wildfire …
wildfire susceptibility is crucial for prevention, early detection, and supporting wildfire …
Modelling, map** and monitoring of forest cover changes, using support vector machine, kernel logistic regression and naive bayes tree models with optical remote …
The present study is designed to monitor the spatio-temporal changes in forest cover using
Remote Sensing (RS) and Geographic Information system (GIS) techniques from 1990 to …
Remote Sensing (RS) and Geographic Information system (GIS) techniques from 1990 to …
[HTML][HTML] Performance evaluation of machine learning methods for forest fire modeling and prediction
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) …
worldwide. This study evaluates the abilities of the Bayes Network (BN), Naïve Bayes (NB) …
Flood susceptible prediction through the use of geospatial variables and machine learning methods
Floods are one of the most perilous natural calamities that cause property destruction and
endanger human life. The spatial patterns of flood susceptibility were assessed in this study …
endanger human life. The spatial patterns of flood susceptibility were assessed in this study …
A novel ensemble modeling approach for the spatial prediction of tropical forest fire susceptibility using LogitBoost machine learning classifier and multi-source …
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 …
reference source in land use planning. We set out to evaluate the use of the LogitBoost …
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 …
Fires dynamics in the Pantanal: Impacts of anthropogenic activities and climate change
JF Marques, MB Alves, CF Silveira, AA e Silva… - Journal of …, 2021 - Elsevier
Anthropogenic activities responsible for modifying climatic regimes and land use and land
cover (LULC) have been altering fire behavior even in regions with natural occurrences …
cover (LULC) have been altering fire behavior even in regions with natural occurrences …
[HTML][HTML] Spatial prediction of groundwater spring potential map** based on an adaptive neuro-fuzzy inference system and metaheuristic optimization
Groundwater is one of the most valuable natural resources in the world (Jha et al., 2007).
However, it is not an unlimited resource; therefore understanding groundwater potential is …
However, it is not an unlimited resource; therefore understanding groundwater potential is …