利用火灾气象指数和热点频率预测占碑省的烧毁面积

Presli Panusunan Simanjuntak
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引用次数: 0

摘要

印度尼西亚每年都会发生森林火灾,其中占碑省是森林火灾高发地区之一。森林火灾造成的重大损失和负面影响促使人们必须及早发现森林和土地火灾,努力预防森林火灾。根据每日输入的气象数据提供森林火灾等级信息的一种方法是火灾气象指数(Fire Weather Index / FWI)系统,该系统最早由加拿大开发。本研究旨在利用温度、降雨量、湿度和风速数据估算占碑地区的燃烧面积。其他辅助数据包括 NASA-FIRMS 卫星提供的热点频率数据,以及 GFED 卫星提供的 2006-2016 年期间每日燃烧面积的数据。本研究采用多元线性回归方法对这些数据变量与燃烧面积估算之间的关系进行了分析,然后对输出模型预测的适用性进行了验证。结果显示,关系最密切的预测变量是热点频率、堆积指数(BUI)和 FWI 指数,相关值分别为 0.888、0.739 和 0.753。烧毁面积的估算模型为烧毁面积 = -966.6146918 + (7.519631195 × BUI) + (147.4865469 × FWI) + (14.5373858 × 热点) + 116,RMSE 值为 635.524,MAE 为 491.38。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediksi Luas Area Terbakar Menggunakan Fire Weather Index dan Frekuensi Titik Panas di Jambi
Forest fires occur every year in Indonesia, one of the regions with the highest forest fires is Jambi Province. Significant losses and negative impacts due to forest fires cause the need for an effort to prevent forest fires early on with the detection of forest and land fires. One method that can provide information about the level of forest fire based on daily weather data input is the Fire Weather Index (Fire Weather Index / FWI) system, which was first developed by Canada. This study aims to estimate burn area in the Jambi region by using temperature, rainfall, humidity, and wind speed data. Other supporting data are hotspot frequency data from NASA-FIRMS satellites and data fraction of burn area from GFED satellites on a daily scale of the period 2006-2016. In this study an analysis of the relationship between these data variables and burn area estimation was carried out using multiple linear regression methods then validated to see the level of suitability of the output model forecasts. The results showed that the predictor variables that had the highest relationship were hotspots frequency, Buildup Index (BUI), and FWI index with correlation values of 0.888, 0.739 and 0.753, respectively. The estimation model of the resulting burnt area is: Burned Area = -966.6146918 + (7.519631195 × BUI) + (147.4865469 × FWI) + (14.5373858 × Hotspots) + 116, with an RMSE value of 635.524 and MAE of 491.38
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