A Review of Forest Fires: Causes, Impacts, and Management Strategies

Authors

  • K. Praveen Kumar Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Guntur, Andhra Pradesh, India, 522302
  • Radhika Rani Chintala Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Guntur, Andhra Pradesh, India, 522302

Keywords:

Forest fires, Natural factors, Human-induced factors, Ecological impacts, Environmental impacts, Socio-economic impacts, Biodiversity

Abstract

This comprehensive review delves into the multifaceted realm of forest fires, exploring their diverse causes, far-reaching impacts, and the array of strategies employed for effective management. Beginning with an examination of the origins of forest fires, we scrutinize both natural and human-induced factors, providing a nuanced understanding of the intricate interplay that leads to ignition. The subsequent section explores the extensive ecological, environmental, and socio-economic impacts of forest fires, shedding light on their implications for biodiversity, air quality, and human communities. Moving beyond the analysis of causation and impact, the review meticulously surveys contemporary and innovative management strategies employed to mitigate and control forest fires. We assess the effectiveness of traditional approaches alongside emerging technologies, such as satellite monitoring, artificial intelligence, and community-based initiatives. Additionally, the review scrutinizes the role of prescribed burning, fire-resistant landscapes, and international collaboration in shaping successful management paradigms. By synthesizing current research findings and drawing on a diverse range of perspectives, this review aims to contribute to a holistic understanding of forest fires. Ultimately, it provides a valuable resource for policymakers, environmentalists, researchers, and the broader community working towards sustainable and resilient ecosystems in the face of this pressing global challenge.

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References

Nelson, R. (2020) Untamedscience.com. April 2019. ]. [Online]. Available: https:// untamedscence. com/ blog/ the- environmental impact of forest- fires/. Accessed 30 December 2020.

Alkhatib, A. A. A review on forest fire detection techniques. Int. J. Distributed Sensor Netw.10, 597368 (2014).

Matin, M.A., Islam, M.M. Overview of wireless sensor network. Intech Open, (2012).

Díaz-Ramírez, A., Tafoya, L.A., Atempa, J.A., Mejía-Alvarez, P. Wireless sensor networks and fusion information methods. In The2012 Iberoamerican Conference on Electronics Engineering and Computer Science,México, (2012).

Molina-Pico, A., Cuesta-Frau, D., Araujo, A., Alejandre, J. & Rozas, A. Forest monitoring and wildland early fire detection by a hierarchical wireless sensor network. J. Sensors 2016, 1–8 (2016).

R. A. A. S. I. H. E. George Emil Sakr, “Forest fire detection wireless sensor node,” in Advances in Forest Fire Research, Imprensa da Universidade de Coimbra, 2014, pp. 1395–1406.

F. F. D. a. P. U. N. w. IoT, “Pantech Solutions,” 4 September 2018. [Online]. Available: https:// www. pante chsol utions. net/ forest- firedetection- and- predi ction- using- nodem cu- with- iot# produ ct_ tabs_ descr iption_ tabbed. Accessed 13 March 2020.

Liu, Y., Liu, Y., Xu, H., Teo, K.L. Forest fire monitoring, detection and decision making systems by wireless sensor network. In 2018Chinese Control And Decision Conference (CCDC), Shenyang, (2018).

Liu, Y., Gu, Y., Chen, G., Ji, Y., Li, J. A novel accurate forest fire detection system using wireless sensor networks. In 2011 Seventh International Conference on Mobile Ad-hoc and Sensor Networks, Japan, (2011).

Bayo, A., Antolín, D., Medrano, N., Calvo, B., Celma, S. Early detection and monitoring of forest fire with a wireless sensor network system. In Procedia Engineering, Spain, (2010).

Alkhatib, A. A. Wireless sensor network for forest fire detection and decision making. Int. J. Adv. Eng. Sci. Technol. 2, 299–310(2013).

Abdullah, S., Bertalan, S., Masar, S., Coskun, A., Kale, I. A Wireless sensor network for early forest fire detection and monitoring as a decision factor in the context of a complex integrated emergency response system. In 2017 IEEE Workshop on Environmental,Energy, and Structural Monitoring Systems (EESMS), Milan, Italy, (2017).

Adnan, A. E. U., Salam, A., Arifin and M. Rizal, Forest fire detection using lora wireless mesh topology. In 2018 2nd East Indonesia Conference on Computer and Information Technology (EIConCIT), Makassar, Indonesia, (2018).

Singh, Y., Saha, S., Chugh, U., Gupta, C. Distributed event detection in wireless sensor networks for forest fires. In 2013 UKSim 15th International Conference on Computer Modelling and Simulation, Cambridge, (2013).

Kadir, E. A., Rosa, S. L., & Yulianti, A. Application of WSNs for detection land and forest fire in Riau Province Indonesia. In 2018 International Conference on Electrical Engineering and Computer Science (ICECOS), Pangkal Pinang, Indonesia, (2018).

Alam, K. M., Kamruzzaman, J., Karmakar, G. & Murshed, M. Dynamic adjustment of sensing range for event coverage in wireless sensor networks. J. Netw. Comput. Appl. 46, 139–153 (2014).

Harrison, D. C., Seah, W. K. & Rayudu, R. Rare event detection and propagation in wireless sensor networks. ACM Computing Surveys. 48, 1 (2016).

Vikram, R., Sinha, D., De, D. & Das, A. K. EEFFL: Energy-efficient data forwarding for forest fire detection using localization techniquein wireless sensor network (Springer, 2020).

Zhu, H., Gao, D., Zhang, S. A perceptron algorithm for forest fire prediction based on wireless sensor networks. Tech Science Press,pp. 25–31, (2019).

Kansal, A., Singh, Y., Kumar, N., Mohindru, V. Detection of forest fires using machine learning technique: A perspective. In ThirdInternational Conference on Image Information Processing, India, (2014).

Zhang, T., Zhao, Q. & Nakamoto, Y. Faulty sensor data detection in wireless sensor networks using logistical regression. In 2017 IEEE 37th International Conference on Distributed Computing Systems Workshops (ICDCSW), Atlanta, (2017).

Fernandez, J. Towards data science. 11 April 2020. [Online]. Available: https:// towar dsdat ascie nce. com/ the- stati stical- analy sis-ttestexpla ined- for- begin ners- and- exper ts- fd0e3 58bbb 62. Accessed 10 February 2021.

Preeti T, Dr. Suvarna Kanakaraddi, Aishwarya Beelagi, Sumalata Malagi, Aishwarya Sudi “Forest Fire Prediction Using Machine Learning Technique”,2021

Pratima Chaubey, Nidhi J. Yadav, Abhishek Chaurasiya “Forest Fire Prediction System using Machine Learning “,2020

Amira. A. Elsonbaty, Ahmed M. Elshewey “Forest fire Detection Using Machine Learning Technique”,2020

Pranjali Bora, Sandeep Sharma, Sandeep Banerjee, K. Sudha, M. Sravanisai, “prevention part I: Prediction and web-based analysis”,2022.

Ayu Shabrina, Intan N. Wahyuni, Rifika Sadikin, Arninda L. Latifah “Evaluation of Random Forest model for forest fire prediction based on climatology overBorneo” 2019.

Salma A Sahel, Samar O Alosaimi, Muhammad Arif, Khloud K Alghamdi Mashael E Alsahafi, Maram A Alharthi and Maryam Arif “Role of Machine Learning in Forest Fire Management” 2021.

Muhammad Arif, Khloud K Alghamdi, Salma A Sahel, Samar O Alosaimi, Mashael E Alsahafi, Maram A Alharthi1 and Maryam Arif

MENG ZHANG, HAO LIANG, AND HAILAN WANG “A Neural Network Model for Wildfire Scale Prediction Using Meteorological Factor

Moreira, et al., A tutorial on synthetic aperture radar, IEEE Geosci. Remote Sens. Mag. 1 (1) (Mar. 2013) 6–43.

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Published

24.03.2024

How to Cite

Kumar, K. P. ., & Chintala, R. R. . (2024). A Review of Forest Fires: Causes, Impacts, and Management Strategies. International Journal of Intelligent Systems and Applications in Engineering, 12(3), 229–234. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5245

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Section

Research Article