Authors : Mayowa B George; Matthew Onuh Ijiga; Oginni Adeyemi

Volume/Issue : Volume 10 - 2025, Issue 3 - March

Google Scholar : https://tinyurl.com/acc6cs5m

Scribd : https://tinyurl.com/4zu475v5

DOI : https://doi.org/10.38124/ijisrt/25mar1859

Google Scholar

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Note : Google Scholar may take 15 to 20 days to display the article.

Abstract : Wildfire prevention and effective grassland burning management rely heavily on accurate Fire Danger Index (FDI) modeling to predict and mitigate fire risks. However, the scarcity and inconsistency of real-world fire data pose significant challenges in developing robust predictive models. This study explores the integration of synthetic data generation algorithms with machine learning to enhance FDI modeling for improved wildfire risk assessment. By leveraging generative adversarial networks (GANs), variational autoencoders (VAEs), and physics-informed neural networks (PINNs), this research aims to generate high-fidelity synthetic fire data that simulate diverse environmental conditions, fuel moisture levels, and ignition patterns. The synthesized datasets augment real-world observations, enabling more accurate FDI computations and predictive analytics. Additionally, we assess the impact of synthetic data augmentation on deep learning- based fire spread simulations to improve early warning systems. The proposed approach enhances decision-making for wildfire prevention, controlled grassland burning, and resource allocation, ultimately contributing to more resilient fire management strategies. The findings highlight the potential of synthetic data-driven methodologies in addressing data limitations, optimizing FDI accuracy, and advancing predictive wildfire risk modeling.

Keywords : Wildfire Prevention, Fire Danger Index (FDI) Modeling, Synthetic Data Generation Algorithms, Predictive Analytics, Grassland Burning Management.

References :

1. Alexander, M. E., & Cruz, M. G. (2013). Limitations on the accuracy of model predictions of wildland fire behaviour: A state-of-the-knowledge overview. The Forestry Chronicle, 89(3), 372-383. https://doi.org/10.5558/tfc2013-067
2. Bauer, A., Trapp, S., Stenger, M., Leppich, R., Kounev, S., Leznik, M., Chard, K., & Foster, I. (2024). Comprehensive exploration of synthetic data generation: A survey. arXiv preprint arXiv:2401.02524.
3. Bradshaw, L. S., Deeming, J. E., Burgan, R. E., & Cohen, J. D. (1984). The 1978 National Fire-Danger Rating System: Technical Documentation. USDA Forest Service, Intermountain Forest and Range Experiment Station.
4. Brockway, D. G., Gatewood, R. G., & Paris, R. B. (2002). Restoring fire as an ecological process in shortgrass prairie ecosystems: initial effects of prescribed burning during the dormant and growing seasons. Journal of Environmental Management, 65(2), 135-152.
5. Cath, C. (2018). Governing artificial intelligence: ethical, legal and technical opportunities and challenges. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2133), 20180080. https://doi.org/10.1098/rsta.2018.0080
6. Cheng, S., Guo, Y., & Arcucci, R. (2023). A generative model for surrogates of spatial-temporal wildfire nowcasting. arXiv preprint arXiv:2308.02810.
7. Dayboro AU. (2025). Understanding the Fire Danger Index: How It’s Calculated and Why It Matters. Retrieved From: https://dayboro.au/fire-danger-index/
8. Elith, J., & Leathwick, J. R. (2009). Species Distribution Models: Ecological Explanation and Prediction Across Space and Time. Annual Review of Ecology, Evolution, and Systematics, 40(1), 677–697. https://doi.org/10.1146/annurev.ecolsys.110308.120159
9. Emery, S. M., & Gross, K. L. (2005). Effects of timing of prescribed fire on the demography of an invasive plant, spotted knapweed (Centaurea maculosa). Journal of Applied Ecology, 42(1), 60-69.
10. Enyejo, J. O., Babalola, I. N. O., Owolabi, F. R. A. Adeyemi, A. F., Osam-Nunoo, G., & Ogwuche, A. O. (2024). Data-driven digital marketing and battery supply chain optimization in the battery powered aircraft industry through case studies of Rolls-Royce’s ACCEL and Airbus's E-Fan X Projects. International Journal of Scholarly Research and Reviews, 2024, 05(02), 001–020.  https://doi.org/10.56781/ijsrr.2024.5.2.0045
11. Enyejo, J. O., Balogun, T. K., Klu, E.  Ahmadu, E. O., & Olola, T. M. (2024). The Intersection of Traumatic Brain Injury, Substance Abuse, and Mental Health Disorders in Incarcerated Women Addressing Intergenerational Trauma through Neuropsychological Rehabilitation. American Journal of Human Psychology (AJHP). Volume 2 Issue 1, Year 2024 ISSN: 2994-8878 (Online). https://journals.e-palli.com/home/index.php/ajhp/article/view/383
12. Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin's Press.
13. Finney, M. A. (2002). Fire growth using minimum travel time methods. Canadian Journal of Forest Research, 32(8), 1420-1424.
14. Friedman, B., & Nissenbaum, H. (1996). Bias in computer systems. ACM Transactions on Information Systems, 14(3), 330–347. https://doi.org/10.1145/230538.230561
15. George, M. B., Okafor, I. O., & Liu, Z. (2024). FIRE DANGER INDEX FOR GRASSLAND PRESCRIBED BURNING MANAGEMENT IN CENTRAL UNITED STATES OF AMERICA (GREAT PLAINS). In 2024 ASABE Annual International Meeting (p. 1). American Society of Agricultural and Biological Engineers.
16. Haas, J. R., Calkin, D. E., & Thompson, M. P. (2013). A national approach for integrating wildfire simulation modeling into Wildland Urban Interface risk assessments within the United States. Landscape and Urban Planning, 119, 44–53. https://doi.org/10.1016/j.landurbplan.2013.06.017
17. Hazra, A., Reich, B. J., Shaby, B. A., & Staicu, A.-M. (2018). A semiparametric spatiotemporal Bayesian model for the bulk and extremes of the Fosberg Fire Weather Index. arXiv preprint arXiv:1812.11699.
18. Idoko, I. P., Ijiga, O. M., Agbo, D. O., Abutu, E. P., Ezebuka, C. I., & Umama, E. E. (2024). Comparative analysis of Internet of Things (IOT) implementation: A case study of Ghana and the USA-vision, architectural elements, and future directions. *World Journal of Advanced Engineering Technology and Sciences*, 11(1), 180-199.
19. Ihimoyan, M. K., Ibokette, A. I., Olumide, F. O., Ijiga, O. M., & Ajayi, A. A. (2024). The Role of AI-Enabled Digital Twins in Managing Financial Data Risks for Small-Scale Business Projects in the United States. International Journal of Scientific Research and Modern Technology, 3(6), 12–40. https://doi.org/10.5281/zenodo.14598498
20. Igba, E., Olarinoye, H. S., Nwakaego, V. E., Sehemba, D. B., Oluhaiyero. Y. S. & Okika, N. (2025). Synthetic Data Generation Using Generative AI to Combat Identity Fraud and Enhance Global Financial Cybersecurity Frameworks. International Journal of Scientific Research and Modern Technology (IJSRMT) Volume 4, Issue 2, 2025. DOI: https://doi.org/10.5281/zenodo.14928919
21. Igba E., Ihimoyan, M. K.,  Awotinwo, B., & Apampa, A. K. (2024). Integrating BERT, GPT, Prophet Algorithm, and Finance Investment Strategies for Enhanced Predictive Modeling and Trend Analysis in Blockchain Technology. Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol., November-December-2024, 10 (6) : 1620-1645.https://doi.org/10.32628/CSEIT241061214 
22. Ijiga, A. C., Aboi, E. J., Idoko, P. I., Enyejo, L. A., & Odeyemi, M. O. (2024). Collaborative innovations in Artificial Intelligence (AI): Partnering with leading U.S. tech firms to combat human trafficking. Global Journal of Engineering and Technology Advances, 2024,18(03), 106-123. https://gjeta.com/sites/default/files/GJETA-2024-0046.pdf
23. Ijiga, A. C., Abutu E. P., Idoko, P.  I., Ezebuka, C. I., Harry, K. D., Ukatu, I. E., & Agbo, D. O. (2024). Technological innovations in mitigating winter health challenges in New York City, USA. International Journal of Science and Research Archive, 2024, 11(01), 535–551. https://ijsra.net/sites/default/files/IJSRA-2024-0078.pdf
24. Ijiga, A. C., Abutu, E. P., Idoko, P. I., Agbo, D. O., Harry, K. D., Ezebuka, C. I., & Umama, E. E. (2024). Ethical considerations in implementing generative AI for healthcare supply chain optimization: A cross-country analysis across India, the United Kingdom, and the United States of America. International Journal of Biological and Pharmaceutical Sciences Archive, 2024, 07(01), 048–063.  https://ijbpsa.com/sites/default/files/IJBPSA-2024-0015.pdf
25. Ijiga, O. M., Idoko, I. P., Ebiega, G. I., Olajide, F. I., Olatunde, T. I., & Ukaegbu, C. (2024). Harnessing adversarial machine learning for advanced threat detection: AI-driven strategies in cybersecurity risk assessment and fraud prevention. Open Access Research Journals. Volume 13, Issue.  https://doi.org/10.53022/oarjst.2024.11.1.0060I
26. Ismail, F. N., & Gharakhanlou, N. M. (2024). An assessment of existing wildfire danger indices in comparison to one-class machine learning models. Natural Hazards. https://doi.org/10.1007/s11069-024-06738-3
27. Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389–399. https://doi.org/10.1038/s42256-019-0088-2
28. Kearney, M., & Porter, W. (2009). Mechanistic niche modelling: Combining physiological and spatial data to predict species' ranges. Ecology Letters, 12(4), 334–350. https://doi.org/10.1111/j.1461-0248.2008.01277.x
29. Koutsias, N., Allgöwer, B., & Kalabokidis, K. (2022). Wildfire danger prediction and understanding with deep learning. Geophysical Research Letters, 49(14), e2022GL099368. https://doi.org/10.1029/2022GL099368
30. Lyle Wallis, Mark Paich. "Integrating artificial intelligence with AnyLogic simulation". Proceedings of the 2017 Winter Simulation Conference.
31. Mark H. (2024). How technology, artificial intelligence are bolstering the battle against wildfires. Retrieved from https://www.reuters.com/sustainability/land-use-biodiversity/how-technology-artificial-intelligence-are-bolstering-battle-against-wildfires-2024-01-03/
32. Matthews, S. (2009). A comparison of fire danger rating systems for use in forests. Australian Meteorological and Oceanographic Journal, 58(1), 41-48. https://doi.org/10.22499/2.5801.005
33. Noble, I. R., Bary, G. A. V., & Gill, A. M. (1980). McArthur's fire-danger meters expressed as equations. Australian Journal of Ecology, 5(2), 201–203. https://doi.org/10.1111/j.1442-9993.1980.tb01243.x
34. Okafor, I. O., George, M. B., & Liu, Z. (2024). PRESCRIBED BURNING RISK QUANTIFICATION: A STEP TOWARDS SMART AND SAFE RANGELAND MANAGEMENT IN THE FLINT HILLS. In 2024 ASABE Annual International Meeting (p. 1). American Society of Agricultural and Biological Engineers.
35. Okeke, R. O., Ibokette, A. I., Ijiga, O. M., Enyejo, L. A., Ebiega, G. I., & Olumubo, O. M. (2024). The reliability assessment of power transformers. *Engineering Science & Technology Journal*, 5(4), 1149-1172.
36. Okika, N. Okoh, O. F., Etuk, E. E. (2025). Mitigating Insider Threats and Social Engineering Tactics in Advanced Persistent Threat Operations through Behavioral Analytics and Cybersecurity Training. International Journal of Advance Research Publication and Reviews. Vol 2, Issue 3, pp 11-27, March 2025.
37. Okika, N., Nwatuzie, G. A., Olarinoye, H. S., Nwaka, A. A., Igba, E. & Dunee, R. (2025). Assessing the Vulnerability of Traditional and Post-Quantum Cryptographic Systems through Penetration Testing and Strengthening Cyber Defenses with Zero Trust Security in the Era of Quantum Computing. International Journal of Innovative Science and Research Technology Volume 10, Issue 2, ISSN No:-2456-2165   https://doi.org/10.5281/zenodo.14959440
38. Okika, N., Nwatuzie, G. A., Odozor, L., Oni, O. & Idoko, I. P., (2025). Addressing IoT-Driven Cybersecurity Risks in Critical Infrastructure to Safeguard Public Utilities and Prevent Large-Scale Service Disruptions. International Journal of Innovative Science and Research Technology. Volume 10, Issue 2, February – 2025 ISSN No:-2456-2165 https://doi.org/ 10.5281/zenodo.14964285   
39. Pang, B., Cheng, S., Huang, Y., Jin, Y., Guo, Y., Prentice, I. C., Harrison, S. P., & Arcucci, R. (2024). Fire-Image-DenseNet (FIDN) for predicting wildfire burnt area using remote sensing data. arXiv preprint arXiv:2412.01400. https://arxiv.org/abs/2412.01400
40. Pérez-Porras, F.-J., Triviño-Tarradas, P., Cima-Rodríguez, C., Meroño-de-Larriva, J.-E., García-Ferrer, A., & Mesas-Carrascosa, F.-J. (2021). Machine learning methods and synthetic data generation to predict large wildfires. Sensors, 21(11), 3694. https://doi.org/10.3390/s21113694
41. Prapas, I., Kondylatos, S., Papoutsis, I., Camps-Valls, G., Ronco, M., Fernández-Torres, M.-Á., Piles Guillem, M., & Carvalhais, N. (2021). Deep learning methods for daily wildfire danger forecasting. arXiv preprint arXiv:2111.02736. https://doi.org/10.48550/arXiv.2111.02736
42. Rodrigues, E., Zadrozny, B., & Watson, C. (2022). Wildfire risk forecast: An optimizable fire danger index. arXiv preprint arXiv:2203.15558.
43. Shaddy, B., Ray, D., Farguell, A., Calaza, V., Mandel, J., Haley, J., Hilburn, K., Mallia, D. V., Kochanski, A., & Oberai, A. (2023). Generative algorithms for fusion of physics-based wildfire spread models with satellite data for initializing wildfire forecasts. arXiv preprint arXiv:2309.02615.
44. Stocks, B. J., Lawson, B. D., Alexander, M. E., Van Wagner, C. E., McAlpine, R. S., Lynham, T. J., & Dube, D. E. (1989). The Canadian Forest Fire Danger Rating System: An overview. The Forestry Chronicle, 65(6), 450–457. https://doi.org/10.5558/tfc65450-6
45. Tam, W. C., Cleary, T., & Fu, E. Y. (2021). Generating synthetic sensor data to facilitate machine learning paradigm for prediction of building fire hazard. Fire Technology, 57(5), 2151-2173. https://doi.org/10.1007/s10694-020-01065-8
46. Xu, Z., Li, J., & Xu, L. (2024). Wildfire risk prediction: A review. arXiv preprint arXiv:2405.01607. https://arxiv.org/abs/2405.01607
47. Yingzhou Lu, Minjie Shen, Huazheng Wang, Xiao Wang, Capucine van Rechem, Tianfan Fu, Wenqi Wei. "Machine Learning for Synthetic Data Generation: A Review". Journal of Latex Class Files, Vol. 14, No. 8, August 2021.
48. Zhao, Z., Kunar, A., Van der Scheer, H., Birke, R., & Chen, L. Y. (2021). CTAB-GAN: Effective table data synthesizing. arXiv preprint arXiv:2102.08369.