Deep neural networks for global wildfire susceptibility modelling

Wildfire susceptibility is of great importance to the prevention and management of global wildfires. Artificial neural networks (ANNs), particularly multilayer perceptrons (MLPs), have been widely used in wildfire susceptibility. Recently, deep neural networks (DNNs) have become state-of-the-art alg...

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Bibliographic Details
Main Authors:	Liu, K. (Author), Wang, M. (Author), Zhang, G. (Author)
Format:	Article
Language:	English
Published:	Elsevier B.V. 2021
Subjects:	2D models artificial neural network Artificial neural networks Classification (of information) Convolution Convolutional neural network Deep neural networks Feedforward neural networks Fires Interpretability modeling Multilayer neural networks Multilayer perceptron neural network Multilayer perceptron neural networks Multilayers Multilayers perceptrons NDVI neighborhood Neural network modelling Neural-networks Pixels Receiver operating characteristic curves soil temperature soil-vegetation interaction spatiotemporal analysis State-of-the-art algorithms wildfire Wildfire susceptibilities Wildfire susceptibility
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Deep neural networks for global wildfire susceptibility modelling

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