Predicting the Number of Forest and Land Fire Hotspot Occurrences Using the ARIMA and SARIMA Methods

Angga Bayu Santoso, Tri Widodo
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Abstract

Forests are an area and part of the environmental cycle that is very important for survival because forests are areas on Earth that regulate the balance of the ecosystem. Forest fires rank second only to illegal logging in Indonesia's list of forest destruction causes. Forest fires can occur due to two factors, namely natural and human factors. Therefore, the hotspot factor that can cause forest fires is an independent variable. The population of hotspots in the West Kalimantan region in 2020 amounted to 1,416 spots. This study aims to predict the number of hotspot occurrences on land and forests that cause fires before the fires spread and are challenging to overcome or extinguish. The method to indicate the number of hotspot occurrences uses the Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA) methods. Modeling ARIMA (0,1,1) and SARIMA (0,1,1) (2,2,1)12 obtained Root Mean Square Error (RMSE) evaluation results for ARIMA of 6.61 while SARIMA of 7.61. The ARIMA's Mean Squared Error (MSE) evaluation value is 43.70, and the SARIMA is 58.05. Based on these results, it can be concluded that the ARIMA model provides excellent and accurate performance in describing the trend of hotspot events that will occur in the future with a smaller RMSE value compared to SARIMA.
使用 ARIMA 和 SARIMA 方法预测森林和陆地火灾热点的发生次数
森林是地球上调节生态系统平衡的一个区域,也是环境循环中对生存非常重要的一部分。在印尼的森林破坏原因排行榜上,森林火灾仅次于非法采伐。森林火灾的发生有两个原因,即自然因素和人为因素。因此,能引起森林火灾的热点因素是一个自变量。2020 年,西加里曼丹地区的热点数量为 1416 个。本研究旨在预测在火势蔓延并难以克服或扑灭之前引发火灾的土地和森林热点事件的数量。预测热点发生数量的方法采用了自回归综合移动平均法(ARIMA)和季节自回归综合移动平均法(SARIMA)。对 ARIMA(0,1,1)和 SARIMA(0,1,1)(2,2,1)12 进行建模后,ARIMA 的均方根误差(RMSE)评估结果为 6.61,而 SARIMA 为 7.61。ARIMA 的均方误差(MSE)评估值为 43.70,SARIMA 为 58.05。基于这些结果,可以得出结论:与 SARIMA 相比,ARIMA 模型的均方误差值较小,在描述未来热点事件的趋势方面具有出色和准确的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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