oa Forest Fire Hazard Forecasting Model Based on Open Remote Sensing Data

References

1. Mohammed, Z., Hanae, S. and SettiL. (2020). Comparative study on machine learning algorithms for early fire forest detection system using geodata. International Journal of Electrical and Computer Engineering (IJECE), 10 (5), 5507–5513. doi: 10.11591/ijece.v10i5.pp5507‑5513
   https://doi.org/10.11591/ijece.v10i5.pp5507-5513 [Google Scholar]
2. Santopaolo, A., Saif, S.S., Pietrabissa, A. and GiuseppiA. (2021, June). Forest Fire Risk Prediction from Satellite Data with Convolutional Neural Networks. Conference paper presented at the 29th Mediterranean Conference on Control and Automation (MED), Bari. doi: 10.1109/MED51440.2021.9480226
   https://doi.org/10.1109/MED51440.2021.9480226 [Google Scholar]
3. Tulchynskyi, V.H., Lavreniuk, S.I., Kolomiiets, O.V., Panchenko, B.Y., Hrechko, A.V., Rohanov, V.Y., …, Denkov, I.D. (2023). Development of an interoperable geoportal infrastructure for two-way data exchange. Stage 3. Develop a prototype of a cloud geoinformation computing subsystem for the geoportal (technical report). V.M. Glushkov Institute of Cybernetics of National Academy of Sciences of Ukraine. Retrieved from https://nrat.ukrintei.ua/searchdoc/0223U005825/ [in Ukrainian].
   [Google Scholar]
4. Vyklyuk, Y., Radovanović, M.M., Stanojević, G., Petrović, M.D., Ćurčić, N.B., Milenković, M., …, Gajić, M. (2020). Connection of Solar Activities and Forest Fires in 2018: Events in the USA (California), Portugal and Greece. Sustainability, 12 (24), 10261. doi: 10.3390/su122410261
   https://doi.org/10.3390/su122410261 [Google Scholar]
5. Zhang, G., Wang, M. and Liu, K. (2021). Deep neural networks for global wildfire susceptibility modeling. Ecological Indicators, 127, 107735. doi: 10.1016/j.ecolind.2021.107735
   https://doi.org/10.1016/j.ecolind.2021.107735 [Google Scholar]
6. Zhang, G., Wang, M., Yang, B. and LiuK. (2024). Current and Future Patterns of Global Wildfire Based on Deep Neural Networks. Earth's Future, 12 (2), e2023EF004088. doi: 10.1029/2023EF004088
   https://doi.org/10.1029/2023EF004088 [Google Scholar]