A Hybrid ARIMA-Intervention Modelling for Forest Fire Risk in The Dry Season

Nurfitri Imro'ah, Nur'ainul Miftahul Huda, Hesty Pratiwi, Muhammad Yahya Ayyash

Abstract


This study explores the time-related patterns of forest fires and assesses the impact of measures implemented during the dry season. Special focus is directed towards the effects of these interventions on the frequency and intensity of fires. This study highlights the importance of combining temporal analysis with spatial data to identify high-risk locations and optimize resource allocation for fire prevention. This study develops an ARIMA model to forecast fire risk before intervention. The findings indicate that integrating intervention factors into the ARIMA model will enhance the model's accuracy. The satisfactory MAPE values and the value data plots effectively demonstrate the data patterns. This method establishes a solid basis for predicting and reducing the risk of forest fires in the dry season, thereby enhancing the fire resilience of ecosystems considered at risk. The findings indicate that the onset of the dry season significantly elevates the risk of forest fires, especially in areas near bodies of water.

Keywords


Accuracy; ARIMA Model; Grid; Step Function

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References


[1] S. Sloan, L. Tacconi, and M. E. Cattau, “Fire prevention in managed landscapes: Recent success and challenges in Indonesia,” Mitigation and Adaptation Strategies for Global Change, vol. 26, no. 7, pp. 32–32, 2021. doi: 10.1007/s11027-021-09965-2.

[2] I. D. Rotherham, Peatlands. Routledge, 2020. doi: 10.4324/9780429439285.

[3] J. Supriatna et al., “Deforestation on the Indonesian island of Sulawesi and the loss of primate habitat,” Global Ecology and Conservation, vol. 24, e01205, 2020. doi: 10.1016/j.gecco.2020.e01205.

[4] D. Arisanty, M. Muhaimin, D. Rosadi, A. N. Saputra, K. P. Hastuti, and I. Rajiani, “Spatiotemporal patterns of burned areas based on the geographic information system for fire risk monitoring,” International Journal of Forestry Research, vol. 2021, pp. 1–10, 2021. doi: 10.1155/2021/2784474.

[5] C. P. R. McCarter et al., “Peat fires and legacy toxic metal release: An integrative biogeochemical and ecohydrological conceptual framework,” Earth-Science Reviews, vol. 256, p. 104867, 2024. doi: 10.1016/j.earscirev.2024.104867.

[6] M. Antala, R. Juszczak, C. van der Tol, and A. Rastogi, “Impact of climate change-induced alterations in peatland vegetation phenology and composition on carbon balance,” Science of The Total Environment, vol. 827, p. 154294, 2022. doi: 10.1016/j.scitotenv.2022.154294.

[7] H. Yu, N. Lu, B. Fu, L. Zhang, M. Wang, and H. Tian, “Hotspots, co-occurrence, and shifts of compound and cascading extreme climate events in Eurasian drylands,” Environment International, vol. 169, p. 107509, 2022. doi: 10.1016/j.envint.2022.107509.

[8] J. Li, Z. Wang, X. Wu, J. Zscheischler, S. Guo, and X. Chen, “A standardized index for assessing sub-monthly compound dry and hot conditions with application in China,” Hydrology and Earth System Sciences, vol. 25, no. 3, pp. 1587–1601, 2021. doi: 10.5194/hess-25-1587-2021.

[9] H. B. Bluestein, F. H. Carr, and S. J. Goodman, “Atmospheric observations of weather and climate,” Atmosphere-Ocean, vol. 60, no. 3–4, pp. 149–187, 2022. doi: 10.1080/07055900.2022.2082369.

[10] P. Reiners, J. Sobrino, and C. Kuenzer, “Satellite-derived land surface temperature dynamics in the context of global change—a review,” Remote Sensing, vol. 15, no. 7, p. 1857, 2023. doi: 10.3390/rs15071857.

[11] R. Andrade-Pacheco et al., “Finding hotspots: Development of an adaptive spatial sampling approach,” Scientific Reports, vol. 10, no. 1, p. 10939, 2020. doi: 10.1038/s41598-020-67666-3.

[12] Y. Pang et al., “Forest fire occurrence prediction in China based on machine learning methods,” Remote Sensing, vol. 14, no. 21, p. 5546, 2022. doi: 10.3390/rs14215546.

[13] Y. Shao et al., “Mapping China’s forest fire risks with machine learning,” Forests, vol. 13, no. 6, p. 856, 2022. doi: 10.3390/f13060856.

[14] A. D. Syphard, S. J. E. Velazco, M. B. Rose, J. Franklin, and H. M. Regan, “The importance of geography in forecasting future fire patterns under climate change,” Proceedings of the National Academy of Sciences, vol. 121, no. 32, 2024. doi: 10.1073/pnas.2310076121.

[15] Y. Cao et al., “Forest fire prediction based on time series networks and remote sensing images,” Forests, vol. 15, no. 7, p. 1221, 2024. doi: 10.3390/f15071221.

[16] E. A. Kadir, H. T. Kung, A. A. AlMansour, H. Irie, S. L. Rosa, and S. S. M. Fauzi, “Wildfire hotspots forecasting and mapping for environmental monitoring based on the long short-term memory networks deep learning algorithm,” Environments, vol. 10, no. 7, p. 124, 2023. doi: 10.3390/environments10070124.

[17] Y. Michael, D. Helman, O. Glickman, D. Gabay, S. Brenner, and I. M. Lensky, “Forecasting fire risk with machine learning and dynamic information derived from satellite vegetation index time-series,” Science of The Total Environment, vol. 764, p. 142844, 2021. doi: 10.1016/j.scitotenv.2020.142844.

[18] M. Naderpour, H. M. Rizeei, and F. Ramezani, “Forest fire risk prediction: A spatial deep neural network-based framework,” Remote Sensing, vol. 13, no. 13, p. 2513, 2021. doi: 10.3390/rs13132513.

[19] Y. Shao et al., “Assessment of China’s forest fire occurrence with deep learning, geographic information and multisource data,” Journal of Forestry Research, vol. 34, no. 4, pp. 963–976, 2023. doi: 10.1007/s11676-022-01559-1.

[20] N. Imro’ah, N. M. Huda, and A. Y. Pratama, “The implementation of control charts as a verification tool in a time series model for COVID-19 vaccine participants in Pontianak,” ComTech: Computer, Mathematics and Engineering Applications, vol. 14, no. 1, pp. 55–67, 2023. doi: 10.21512/comtech.v14i1.8462.

[21] N. Imro’ah, N. M. Huda, D. S. Utami, T. Umairah, and N. F. Arini, “Control chart for correcting the ARIMA time series model of GDP growth cases,” JTAM (Jurnal Teori dan Aplikasi Matematika), vol. 8, no. 1, p. 312, 2024. doi: 10.31764/jtam.v8i1.19612.

[22] N. Imro’ah and N. M. Huda, “Control chart as verification tools in time series model,” BAREKENG: Jurnal Ilmu Matematika dan Terapan, vol. 16, no. 3, pp. 995–1002, 2022. doi: 10.30598/barekengvol16iss3pp995-1002.

[23] E. J. Inyang, N. M. Nafo, A. I. Wegbom, and Y. A. Da-Wariboko, “ETS - ARIMA intervention modelling of Bangladesh Taka/Nigerian Naira exchange rates,” Science Journal of Applied Mathematics and Statistics, 2024. doi: 10.11648/j.sjams.20241201.11.

[24] R. D. Ilmiah and S. I. Oktora, “ARIMA intervention model for measuring the impact of the lobster seeds fishing and export ban policy on the Indonesian lobster export,” Journal of Physics: Conference Series, vol. 2123, no. 1, p. 012011, 2021. doi: 10.1088/1742-6596/2123/1/012011.

[25] E. J. Inyang, E. H. Etuk, N. M. Nafo, and Y. A. Da-Wariboko, “Time series intervention modelling based on ESM and ARIMA models: Daily Pakistan Rupee/Nigerian Naira exchange rate,” Asian Journal of Probability and Statistics, vol. 25, no. 3, pp. 1–17, 2023. doi: 10.9734/ajpas/2023/v25i3560.

[26] S. Dorais, “Time series analysis in preventive intervention research: A step-by-step guide,” Journal of Counseling & Development, vol. 102, no. 2, pp. 239–250, 2024. doi: 10.1002/jcad.12508.

[27] N. Imro’ah and N. M. Huda, “Double intervention analysis on the ARIMA model of COVID-19 cases in Bali,” Journal of the Indonesian Mathematical Society, vol. 31, no. 1, p. 1347, 2025. doi: 10.22342/jims.v31i1.1347.




DOI: https://doi.org/10.18860/cauchy.v10i2.36741

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