Global Journal of Engineering and Technology Research (GJETR)
Forecasting Fire Incidents and Direct Property Losses in Vietnam: A Comparative Time-Series Study
Nguyen Thi Lan Anh
25 April 2026 · Vol. 2, Issue 4, pp. 115-120
DOI: 10.65150/EP-gjetr/V2E4/2026-02
Abstract
This article compares several short-term time-series models for forecasting fire incidents and direct property losses caused by fire in Vietnam. The study uses annual data for the 2020–2025 period compiled from official releases and published materials of the Fire Prevention, Firefighting and Rescue Police Department. Five forecasting methods are employed: the average absolute increase method, the average growth rate method, linear trend regression, simple exponential smoothing, and the three-year moving average. The results show that the three-year moving average model is the most suitable for both indicators. According to this model, Vietnam may record approximately 3,568 fire incidents and VND 774.08 billion in direct property losses in 2026. The article also indicates that although the share of direct fire-related property losses in GDP remains small, the actual economic impact of fire on firms, households, and supply chains is substantial. The study therefore underscores the role of fire forecasting in fire prevention, firefighting, rescue operations, and economic risk governance.
Keywords: fire incidents, direct property losses, time-series models, short-term forecasting, fire prevention, firefighting and rescue, Vietnam
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Cite this article
Nguyen Thi Lan Anh (2026). Forecasting Fire Incidents and Direct Property Losses in Vietnam: A Comparative Time-Series Study. Global Journal of Engineering and Technology Research, 2(4), 115-120. https://doi.org/10.65150/EP-gjetr/V2E4/2026-02
@article{Nguyen2026,
title = {Forecasting Fire Incidents and Direct Property Losses in Vietnam: A Comparative Time-Series Study},
author = {Nguyen Thi Lan Anh},
journal = {Global Journal of Engineering and Technology Research},
year = {2026},
volume = {2},
number = {4},
pages = {115-120},
doi = {10.65150/EP-gjetr/V2E4/2026-02},
url = {https://doi.org/10.65150/EP-gjetr/V2E4/2026-02}
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