Combustion machine learning: Principles, progress and prospects
Progress in combustion science and engineering has led to the generation of large amounts
of data from large-scale simulations, high-resolution experiments, and sensors. This corpus …
of data from large-scale simulations, high-resolution experiments, and sensors. This corpus …
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] Application of remote sensing and machine learning algorithms for forest fire mapping in a Mediterranean area
Forest fire disaster is currently the subject of intense research worldwide. The development
of accurate strategies to prevent potential impacts and minimize the occurrence of disastrous …
of accurate strategies to prevent potential impacts and minimize the occurrence of disastrous …
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 …
Spatio-temporal estimation of the daily cases of COVID-19 in worldwide using random forest machine learning algorithm
CM Yeşilkanat - Chaos, Solitons & Fractals, 2020 - Elsevier
Novel Coronavirus pandemic, which negatively affected public health in social,
psychological and economical terms, spread to the whole world in a short period of 6 …
psychological and economical terms, spread to the whole world in a short period of 6 …
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 …
Assessing the robustness of Random Forests to map land cover with high resolution satellite image time series over large areas
New remote sensing sensors will acquire High spectral, spatial and temporal Resolution
Satellite Image Time Series (HR-SITS). These new data are of great interest to map land …
Satellite Image Time Series (HR-SITS). These new data are of great interest to map land …
GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran
SA Naghibi, HR Pourghasemi, B Dixon - Environmental monitoring and …, 2016 - Springer
Groundwater is considered one of the most valuable fresh water resources. The main
objective of this study was to produce groundwater spring potential maps in the Koohrang …
objective of this study was to produce groundwater spring potential maps in the Koohrang …
Forest fire occurrence prediction in China based on machine learning methods
Forest fires may have devastating consequences for the environment and for human lives.
The prediction of forest fires is vital for preventing their occurrence. Currently, there are fewer …
The prediction of forest fires is vital for preventing their occurrence. Currently, there are fewer …
Application of GIS-based data driven random forest and maximum entropy models for groundwater potential mapping: a case study at Mehran Region, Iran
O Rahmati, HR Pourghasemi, AM Melesse - Catena, 2016 - Elsevier
Groundwater is considered as the most important natural resources in arid and semi-arid
regions. In this study, the application of random forest (RF) and maximum entropy (ME) …
regions. In this study, the application of random forest (RF) and maximum entropy (ME) …