确定气候条件对地中海地区森林火灾风险影响的新方法tÜrkİye

Semra Türkan, G. Özel, C. O. Güney, Ceren Ünal, Özdemir Şentürk, K. Özkan
{"title":"确定气候条件对地中海地区森林火灾风险影响的新方法tÜrkİye","authors":"Semra Türkan, G. Özel, C. O. Güney, Ceren Ünal, Özdemir Şentürk, K. Özkan","doi":"10.22531/muglajsci.1273256","DOIUrl":null,"url":null,"abstract":"The risk of forest fires is a major problem in Türkiye's Mediterranean region and has a significant impact on ecosystems and atmospheric conditions. Throughout the previous century, a significant portion of Türkiye's Mediterranean Region has been destroyed by forest fires. This study aims to determine the meteorological covariates, such as relative humidity, maximum temperature, and wind speed, that affect forest fires. We classified forest fires into two groups. The first group (F1) refers to small forest fires, with burned forest areas of less than 10 hectares. The second group (F2), representing rare events, corresponds to burned areas of more than 10 hectares. The data is composed of binary values (F1=0 and F2=1) taken between the years 2015-2019 from different locations in the Mediterranean Region of Türkiye. For binary data modeling, the ordinary logistic regression (LR) has been frequently used. However, such a method tends to give biased results when using rare event data. Therefore, we employed three different modeling techniques specifically designed for rare event data. According to the results obtained from the best model, Firth's Logistic Regression (FLR), wind speed, and maximum temperature are found to be statistically significant variables in the occurrence of forest fires greater than 10 hectares.","PeriodicalId":149663,"journal":{"name":"Mugla Journal of Science and Technology","volume":"212 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A NEW APPROACH TO DETERMINE THE INFLUENCE OF WEATHER CONDITIONS ON FOREST FIRE RISK IN THE MEDITERRANEAN REGION OF TÜRKİYE\",\"authors\":\"Semra Türkan, G. Özel, C. O. Güney, Ceren Ünal, Özdemir Şentürk, K. Özkan\",\"doi\":\"10.22531/muglajsci.1273256\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The risk of forest fires is a major problem in Türkiye's Mediterranean region and has a significant impact on ecosystems and atmospheric conditions. Throughout the previous century, a significant portion of Türkiye's Mediterranean Region has been destroyed by forest fires. This study aims to determine the meteorological covariates, such as relative humidity, maximum temperature, and wind speed, that affect forest fires. We classified forest fires into two groups. The first group (F1) refers to small forest fires, with burned forest areas of less than 10 hectares. The second group (F2), representing rare events, corresponds to burned areas of more than 10 hectares. The data is composed of binary values (F1=0 and F2=1) taken between the years 2015-2019 from different locations in the Mediterranean Region of Türkiye. For binary data modeling, the ordinary logistic regression (LR) has been frequently used. However, such a method tends to give biased results when using rare event data. Therefore, we employed three different modeling techniques specifically designed for rare event data. According to the results obtained from the best model, Firth's Logistic Regression (FLR), wind speed, and maximum temperature are found to be statistically significant variables in the occurrence of forest fires greater than 10 hectares.\",\"PeriodicalId\":149663,\"journal\":{\"name\":\"Mugla Journal of Science and Technology\",\"volume\":\"212 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-07-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Mugla Journal of Science and Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.22531/muglajsci.1273256\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mugla Journal of Science and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.22531/muglajsci.1273256","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

森林火灾的风险是基耶岛地中海地区的一个主要问题,对生态系统和大气条件有重大影响。在上个世纪,土耳其的地中海地区有很大一部分被森林火灾摧毁。本研究旨在确定影响森林火灾的气象协变量,如相对湿度、最高温度和风速。我们把森林火灾分为两类。第一组(F1)为小型森林火灾,被烧毁森林面积小于10公顷。第二组(F2)代表罕见事件,对应于超过10公顷的烧毁区域。数据由2015-2019年期间从基耶省地中海地区不同地点采集的二进制值(F1=0和F2=1)组成。对于二元数据建模,通常使用普通逻辑回归(LR)。然而,当使用罕见事件数据时,这种方法往往会给出有偏差的结果。因此,我们采用了三种专门为罕见事件数据设计的不同建模技术。根据最佳模型的结果,发现Firth's Logistic回归(FLR)、风速和最高温度是10公顷以上森林火灾发生的统计显著变量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A NEW APPROACH TO DETERMINE THE INFLUENCE OF WEATHER CONDITIONS ON FOREST FIRE RISK IN THE MEDITERRANEAN REGION OF TÜRKİYE
The risk of forest fires is a major problem in Türkiye's Mediterranean region and has a significant impact on ecosystems and atmospheric conditions. Throughout the previous century, a significant portion of Türkiye's Mediterranean Region has been destroyed by forest fires. This study aims to determine the meteorological covariates, such as relative humidity, maximum temperature, and wind speed, that affect forest fires. We classified forest fires into two groups. The first group (F1) refers to small forest fires, with burned forest areas of less than 10 hectares. The second group (F2), representing rare events, corresponds to burned areas of more than 10 hectares. The data is composed of binary values (F1=0 and F2=1) taken between the years 2015-2019 from different locations in the Mediterranean Region of Türkiye. For binary data modeling, the ordinary logistic regression (LR) has been frequently used. However, such a method tends to give biased results when using rare event data. Therefore, we employed three different modeling techniques specifically designed for rare event data. According to the results obtained from the best model, Firth's Logistic Regression (FLR), wind speed, and maximum temperature are found to be statistically significant variables in the occurrence of forest fires greater than 10 hectares.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信