Harnessing deep learning for wildfire risks prediction: A novel approach

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PDF Hoang Anh Duc
Published: Jun 19, 2023
DOI: https://doi.org/10.26483/ijarcs.v14i3.6983
Keywords:
Deep learning, CNNs, RNNs, ADAM, Wildfires, Geospatial data analysis

Main Article Content

Duc Anh Hoang
https://orcid.org/0009-0004-6028-1999
Le Van Hung, Nguyen Thi Huu Phuong

Abstract

This article presents a pioneering approach for predicting wildfires risks using deep learning techniques. By combining convolutional neural networks (CNNs), recurrent neural networks (RNNs) and Adaptive Moment Estimation (ADAM), our framework analyzes geospatial and environmental data to capture the intricate dynamics of disasters. Our model integrates satellite imagery, climate data, socioeconomic factors, and historical records to accurately assess risks. Leveraging transfer learning, we optimize training efficiency with pre-trained models. Extensive experiments demonstrate the superior performance of our deep learning framework compared to traditional methods. With its ability to enable proactive planning and decision-making, our approach strengthens disaster preparedness and response strategies. This research represents a significant advancement in utilizing deep learning for predicting wildfires risks, paving the way for further innovations in this vital field.

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Vol. 14 No. 3 (2023): May-June 2023
Section
Articles

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References

D. T. Bui, Q.-T. Bui, Q.-P. Nguyen, B. Pradhan, H. Nampak, and P. T. Trinh, "A hybrid artificial intelligence approach using GIS-based neural-fuzzy inference system and particle swarm optimization for forest fire susceptibility modeling at a tropical area," Agricultural and Forest Meteorology, vol. 233, pp. 32-44, 2017. [Online]. Available: https://doi.org/10.1016/j.agrformet.2016.11.002

V. T. Dien, P. N. Bay, P. Stephen, T. V. Chau, A. Grais, and S. Petrova, "Land Use, Forest Cover Change and Historical GHG Emission from 1990 to 2010, Lam Dong province, Vietnam," USAID, LEAF Hanoi, 2013.

P. E. Higuera, J. T. Abatzoglou, J. S. Littell, and P. Morgan, "The Changing Strength and Nature of Fire-Climate Relationships in the Northern Rocky Mountains, U.S.A., 1902-2008," Published June 26, 2015. [Online]. Available: https://doi.org/10.1371/journal.pone.0127563

D. Tien Bui, K.-T. Le, V. Nguyen, H. Le, and I. Revhaug, “Tropical Forest Fire Susceptibility Mapping at the Cat Ba National Park Area, Hai Phong City, Vietnam, Using GIS-Based Kernel Logistic Regression,†Remote Sensing, vol. 8, no. 4, p. 347, Apr. 2016, doi: 10.3390/rs8040347.

N. L. Dan, N. T. Hieu, and V. T. T. Lan, "Drought, desertification in Tay Nguyen territory associated with climate change scenarios," J. Earth Sci., vol. 35, no. 4, pp. 310-317, 2014.

J. C. Verde and J. L. Zêzere, "Assessment and validation of wildfire susceptibility and hazard in Portugal," Nat. Hazards Earth Syst. Sci., vol. 10, no. 3, pp. 485-497, 2010. [Online]. Available: https://nhess.copernicus.org/articles/10/485/2010/. [Accessed: 01/01/2023].

A. Camp, C. Oliver, P. Hessburg, and R. Everett, "Predicting late-successional fire refugia pre-dating European settlement in the Wenatchee Mountains," Forest Ecology and Management, vol. 95, no. 1, pp. 63-77, 1997. [Online]. Available: https://doi.org/10.1016/S0378-1127(97)00006-6. Accessed on: Jan. 1, 2023.

D.A. Schmidt, A.H. Taylor, and C.N. Skinner, "The influence of fuels treatment and landscape arrangement on simulated fire behavior, Southern Cascade range, California," Forest Ecology and Management, vol. 255, no. 8, pp. 3170–3184, 2008.

M. Huesca, J. Litago, A. Palacios-Orueta, F. Montes, A. Sebastián-López, and P. Escribano, "Assessment of forest fire seasonality using MODIS fire potential: A time series approach," Agricultural and Forest Meteorology, vol. 149, no. 11, pp. 1946-1955, 2009. [Online]. Available: https://doi.org/10.1016/j.agrformet.2009.06.022. Accessed: Jan. 1, 2023.

D. Nepstad, C. Stickler, B. Filho, and F. Merry, "Interactions among Amazon Land Use, Forests and Climate: Prospects for a Near-Term Forest Tipping Point," Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, vol. 363, pp. 1737-1746, 2008. [Online]. Available: https://doi.org/10.1098/rstb.2007.0036. Accessed: Dec. 22, 2022.

S. Bajocco, E. Dragoz, I. Gitas, D. Smiraglia, L. Salvati, "Mapping Forest Fuels through Vegetation Phenology: The Role of Coarse-Resolution Satellite Time-Series," PLOS ONE, vol. 10, no. 3, pp. e0119811, 2015. [Online]. Available: https://doi.org/10.1371/journal.pone.0119811. Accessed: Dec. 22, 2022.

K. Yi, H. Tani, J. Zhang, M. Guo, X. Wang, and G. Zhong, "Long-Term Satellite Detection of Post-Fire Vegetation Trends in Boreal Forests of China," Remote Sensing, vol. 5, no. 12, pp. 6938-6957, 2013. [Online]. Available: https://doi.org/10.3390/rs5126938. Accessed: Dec. 22, 2022

H. Le, T. Nguyen, K. Lasko, S. Ilavajhala, K. Vadrevu, and C. Justice, "Vegetation fires and air pollution in Vietnam," Environmental Pollution, vol. 195, pp. 10-19, 2014. [Online]. Available: https://doi.org/10.1016/j.envpol.2014.07.023. Accessed: Dec. 22, 2022

N. Gillett, A. Weaver, F. Zwiers, and M. Flannigan, "Detecting the effect of climate change on Canadian forest fires," Geophysical Research Letters, vol. 31, Sep. 2004, doi: 10.1029/2004GL020876. Accessed: Dec. 22, 2022

M. Heimann and M. Reichstein, "Terrestrial ecosystem carbon dynamics and climate feedbacks," Nature, vol. 451, pp. 289-292, Jan. 2008. [Online]. Available: https://www.nature.com/articles/nature06591. Accessed: Dec. 22, 2022.

B. Zaitchik, J. Santanello, S. Kumar, and C. Peters-Lidard, "Representation of soil moisture feedbacks during drought in NASA unified WRF (NU-WRF)," Journal of Hydrometeorology, vol. 14, pp. 360-367, 2013. [Online]. Available: https://doi.org/10.1175/JHM-D-12-069.1. Accessed: Dec. 22, 2023.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet classification with deep convolutional neural networks," in Advances in Neural Information Processing Systems, 2012, pp. 1097-1105. Accessed: Dec. 22, 2023.

Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780. doi: 10.1162/neco.1997.9.8.1735. Accessed: Dec. 22, 2023.

D. P. Kingma and J. Ba, "Adam: A method for stochastic optimization," in International Conference on Learning Representations (ICLR), 2015. [Online]. Available: https://arxiv.org/abs/1412.6980 (Accessed: 22/12/2023).

S. Ruder, "An overview of gradient descent optimization algorithms," arXiv preprint arXiv:1609.04747, 2016. [Online]. Available: https://arxiv.org/abs/1609.04747 (Accessed: 22/12/2023).

T. Tieleman and G. Hinton, "RMSprop: Divide the gradient by a running average of its recent magnitude," COURSERA: Neural Networks for Machine Learning, 2012. [Online]. Available: http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf (Accessed: 22/12/2023).