Prevention and Treatment of Natural Disasters

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Advancing Forest-Fire Management: Exploring Sensor Networks, Data Mining Techniques, and SVM Algorithm for Prediction

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https://doi.org/10.54963/ptnd.v3i2.271
Zhang, S., & Pan, M. (2024). Advancing Forest-Fire Management: Exploring Sensor Networks, Data Mining Techniques, and SVM Algorithm for Prediction. Prevention and Treatment of Natural Disasters, 3(2). https://doi.org/10.54963/ptnd.v3i2.271
Volume 3 Issue 2 (2024): In Progress
Copyright (c) 2024 shuo zhang, Mengya Pan
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Authors

Forest-fire is a pressing global problem that has far-reaching effects on human life and the environment, with climate change exacerbating their frequency and intensity. There is an urgent need for advanced predictive systems to mitigate these impacts. To address this issue, this study introduces a forest-fire prediction framework integrating wireless sensor networks (WSNs), data analysis, and machine learning. Sensor nodes deployed in a forest area collected real-time meteorological data, which was transmitted using LoRaWAN technology. Data mining techniques prepared the data for analysis using the SVM algorithm, revealing relationships between meteorological parameters and wildfire risk. The SVM model demonstrated an accuracy of 86% in classifying forest-fire risk levels based on temperature, humidity, wind speed, and rainfall data. The integrated framework of WSNs and the SVM algorithm provides a high-accuracy model for forest-fire risk prediction. The model is compared to the Canadian Forest Fire Hazard Rating System to validate its accuracy, demonstrating strong agreement with historical records and reports. The model's practical implications include efficient management, early detection, and prevention strategies. However, the model's limitations suggest avenues for future research, we should consider broader geographic applications and using advanced machine-learning methods to enhance the model's predictive capabilities.

Keywords:

forest-fire prediction wireless sensor networks data analysis machine learning meteorological parameters

Highlights

  • Innovative research methodology: The paper illustrates the pivotal role of wireless sensor networks (WSNs) in gathering crucial meteorological data for forest fire prediction.
  • Groundbreaking findings: The paper demonstrates the effectiveness of machine learning algorithms, specifically the Support Vector Machine (SVM), in analyzing environmental parameters for wildfire risk assessment.
  • Practical implications for the field: The paper reveals that the integration of WSNs with advanced data analysis techniques significantly improves the accuracy of forest-fire prediction models.

References

  1. Lin, X.; Li, Z.; Chen, W.; Sun, X.; Gao, D. Forest Fire Prediction Based on Long-and Short-Term Time-Series Network. Forests 2023, 14, 778.
  2. McMichael, C.N. Ecological Legacies of Past Human Activities in Amazonian Forests. New Phytol. 2021, 229, 2492–2496.
  3. Pérez-Invernón, F.J.; Gordillo-Vázquez, F.J.; Huntrieser, H.; Jöckel, P. Variation of Lightning-Ignited Wildfire Patterns under Climate Change. Nat. Commun. 2023, 14, 739.
  4. Dye, A.W.; Gao, P.; Kim, J.B.; Lei, T.; Riley, K.L.; Yocom, L. High-Resolution Wildfire Simulations Reveal Complexity of Climate Change Impacts on Projected Burn Probability for Southern California. Fire Ecol. 2023, 19, 1–19.
  5. Kala, C.P. Environmental and socioeconomic impacts of forest fires: A Call for Multilateral Cooperation and Management Interventions. Nat. Hazards Res. 2023, 3, 286–294.
  6. Mandal, A.; Nykiel, G.; Strzyzewski, T.; Kochanski, A.; Wrońska, W.; Gruszczynska, M.; Figurski, M. High-Resolution Fire Danger Forecast for Poland Based on the Weather Research and Forecasting Model. Int. J. Wildland Fire 2021, 31, 149–162.
  7. Venäläinen, A.; Lehtonen, I.; Laapas, M.; Ruosteenoja, K.; Tikkanen, O.P.; Viiri, H.; Viiri, H.; Peltola, H. Climate Change Induces Multiple Risks to Boreal Forests and Forestry in Finland: A literature review. Global Change Biol. 2020, 26, 4178–4196.
  8. Xie, Y.; Peng, M. Forest Fire Forecasting Using Ensemble Learning Approaches. Neural Comput. Appl. 2019, 31, 4541–4550.
  9. Alkhatib, R.; Sahwan, W.; Alkhatieb, A.; Schütt, B. A Brief Review of Machine Learning Algorithms in Forest Fires Science. Appl. Sci. 2023, 13, 8275.
  10. Abid, F. A Survey of Machine Learning Algorithms Based Forest Fires Prediction and Detection Systems. Fire Technol. 2021, 57, 559–590.
  11. Arif, M.; Alghamdi, K.K.; Sahel, S.A.; Alosaimi, S.O.; Alsahaft, M.E.; Alharthi, M.A.; Arif, M. Role of Machine Learning Algorithms in Forest Fire Management: A literature review. J. Robot. Autom. 2021, 5, 212–226.
  12. Gale, M.G.; Cary, G.J.; Van Dijk, A.I.; Yebra, M. Forest Fire Fuel through the Lens of Remote Sensing: Review of Approaches, Challenges and Future Directions in the Remote Sensing of Biotic Determinants of Fire Behaviour. Remote Sens. Environ. 2021, 255, 112282.
  13. Ananthi, J.; Sengottaiyan, N.; Anbukaruppusamy, S.; Upreti, K.; Dubey, A.K. Forest Fire Prediction Using IoT and Deep Learning. Int. J. Advanced Technol. Eng. Explor. 2022, 9, 246–256.
  14. Yandouzi, M.; Grari, M.; Berrahal, M.; Idrissi, I.; Moussaoui, O.; Azizi, M.; Ghoumid, K.; Elmiad, A.K. Investigation of Combining Deep Learning Object Recognition with Drones for Forest Fire Detection and Monitoring. Int. J. Adv. Comput. Sci. Appl. 2023, 14, 377–384.
  15. Jaafari, A.; Zenner, E.K.; Panahi, M.; Shahabi, H. Hybrid Artificial Intelligence Models Based on a Neuro-Fuzzy System and Metaheuristic Optimization Algorithms for Spatial Prediction of Wildfire Probability. Agric. For. Meteorol. 2019, 266, 198–207.
  16. Garg, S.; Aryal, J.; Wang, H.; Shah, T.; Kecskemeti, G.; Ranjan, R. Cloud Computing Based Bushfire Prediction for Cyber—Physical Emergency Applications. Future Gener. Comput. Syst. 2018, 79, 354–363.
  17. Zhou, T.; Li, B.; Wu, C.; Tan, Y.; Mao, L.; Wu, W. Studies on Big Data Mining Techniques in wildfire Prevention for Power System. In Proceedings of the 2019 IEEE 3rd Conference on Energy Internet and Energy System Integration (EI2), Changsha, China, 8–10 November 2019.
  18. Abid, F.; Izeboudjen, N. Predicting Forest Fire in Algeria using Data Mining Techniques: Case study of the Decision Tree Algorithm. In Proceedings of the International Conference on Advanced Intelligent Systems for Sustainable Development, Marrakech, Morocco, 8–11 July 2019.
  19. Sayad, Y.O.; Mousannif, H.; Al Moatassime, H. Predictive Modeling of Wildfires: A new Dataset and Machine Learning Approach. Fire Saf. J. 2019, 104, 130–146.
  20. Girtsou, S.; Apostolakis, A.; Giannopoulos, G.; Kontoes, C. A Machine Learning Methodology for Next Day Wildfire Prediction. In Proceedings of the 2021 IEEE International Symposium on Geoscience and Remote Sensing, Brussels, Belgium, 11–16 July 2021.
  21. Iban, M.C.; Sekertekin, A. Machine Learning Based Wildfire Susceptibility Mapping Using Remotely Sensed Fire Data and GIS: A Case Study of Adana and Mersin Provinces, Turkey. Ecol. Inf. 2022, 69, 101647.
  22. Turco, M.; Abatzoglou, J.T.; Herrera, S.; Zhuang, Y.; Jerez, S.; Lucas, D.D.; AghaKouchak, A.; Cvijanovic, I. Anthropogenic Climate Change Impacts Exacerbate Summer Forest Fires in California. Proc. Natl. Acad. Sci. 2023, 120.
  23. Feizizadeh, B.; Omarzadeh, D.; Mohammadnejad, V.; Khallaghi, H.; Sharifi, A.; Karkarg, B.G. An integrated Approach of Artificial Intelligence and Geoinformation Techniques Applied to Forest Fire Risk Modeling in Gachsaran, Iran. J. Environ. Plann. Manage. 2023, 66, 1369–1391.
  24. Masinda, M.M.; Li, F.; Qi, L.; Sun, L.; Hu, T. Forest Fire Risk Estimation in a Typical Temperate Forest in Northeastern China Using the Canadian Forest Fire Weather Index: Case Study in Autumn 2019 and 2020. Nat. Hazards 2022, 111, 1085–1101.
  25. Pan, M.; Zhang, S. Visualization of Prediction Methods for Wildfire Modeling Using CiteSpace: A Bibliometric Analysis. Atmosphere 2023, 14, 1009.
  26. Guan, Z.; Miao, X.; Mu, Y.; Sun, Q.; Ye, Q.; Gao, D. Forest Fire Segmentation from Aerial Imagery Data Using an Improved Instance Segmentation Model. Remote Sens. 2022, 14, 3159.
  27. Kolanek, A.; Szymanowski, M.; Raczyk, A. Human Activity Affects Forest Fires: The impact of Anthropogenic Factors on the Density of Forest Fires in Poland. Forests 2021, 12, 728.
  28. Harvey, B.J. Human-Caused Climate Change is Now a Key Driver of Forest Fire Activity in the Western United States. Proc. Natl. Acad. Sci. 2016, 113, 11649–11650.
  29. Demin, G.; Haifeng, L.; Anna, J.; Guoxin, W. A Forest Fire Prediction System Based on Rechargeable Wireless Sensor Networks. In Proceedings of the 2014 4th IEEE International Conference on Network Infrastructure and Digital Content, Beijing, China, 19–21 September 2014.
  30. Dampage, U.; Bandaranayake, L.; Wanasinghe, R.; Kottahachchi, K.; Jayasanka, B. Forest Fire Detection System Using Wireless Sensor Networks and Machine Learning. Sci. Rep. 2022, 12, 46.
  31. Apriani, Y.; Oktaviani, W.A.; Sofian, I.M. Design and Implementation of LoRa-Based Forest Fire Monitoring System. J. Rob. Control 2022, 3, 236–243.
  32. Wood, D.A. Prediction and Data Mining of Burned Areas of Forest Fires: Optimized Data Matching and Mining Algorithm Provides Valuable Insight. Artif. Intell. Agric. 2021, 5, 24–42.
  33. Jain, P.; Coogan, S.C.; Subramanian, S.G.; Crowley, M.; Taylor, S.; Flannigan, M.D. A Review of Machine Learning Applications in Wildfire Science and Management. Environ. Rev. 2020, 28, 478–505.