A comparative Bayesian optimization-based machine learning and artificial neural networks approach for burned area prediction in forest fires: an application in Turkey
Author:
Publisher
Springer Science and Business Media LLC
Subject
Earth and Planetary Sciences (miscellaneous),Atmospheric Science,Water Science and Technology
Link
https://link.springer.com/content/pdf/10.1007/s11069-023-06187-4.pdf
Reference62 articles.
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3. Alade IO, Abd Rahman MA, Saleh TA (2019) Predicting the specific heat capacity of alumina/ethylene glycol nanofluids using support vector regression model optimized with Bayesian algorithm. Sol Energy 183:74–82
4. Amos C, Petropoulos GP, Ferentinos KP (2019) Determining the use of Sentinel-2A MSI for wildfire burning & severity detection. Int J Remote Sens 40(3):905–930
5. Anshori M, Mar'i F, Alauddin M W, & Mahmudy W F (2019) Prediction of forest fire using neural network based on extreme learning machines (ELM). In: 2019 International conference on sustainable information engineering and technology (SIET) (pp. 301–305). IEEE
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