基于梯度增强决策树的旅游景区生态健康评价

IF 0.5 Q4 ENGINEERING, ENVIRONMENTAL
Renzhong Jin
{"title":"基于梯度增强决策树的旅游景区生态健康评价","authors":"Renzhong Jin","doi":"10.1504/ijetm.2023.134325","DOIUrl":null,"url":null,"abstract":"In order to overcome many problems existing in traditional evaluation methods, such as the low accuracy of the evaluation of ecological health of tourist attractions, an ecological health evaluation method of tourist attractions based on gradient boosting decision tree was proposed. The data collection framework of tourist attractions based on UAV low-altitude remote sensing is designed, the ecological health evaluation index system of tourist attractions is constructed, and information entropy and analytic hierarchy process were used to determine the combination weight. The gradient boosting decision tree algorithm is used to calculate the ecological health of tourist attractions, and multiple support vector machines are used to construct multi-classifiers to achieve ecological health evaluation. The experimental results show that the average data acquisition time of the method in this paper is 0.76 s, the error rate of the index weight calculation is between -1% and 2%, and the average evaluation accuracy rate is 97.2%.","PeriodicalId":13984,"journal":{"name":"International Journal of Environmental Technology and Management","volume":null,"pages":null},"PeriodicalIF":0.5000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An ecological health evaluation of tourist attractions based on gradient boosting decision tree\",\"authors\":\"Renzhong Jin\",\"doi\":\"10.1504/ijetm.2023.134325\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In order to overcome many problems existing in traditional evaluation methods, such as the low accuracy of the evaluation of ecological health of tourist attractions, an ecological health evaluation method of tourist attractions based on gradient boosting decision tree was proposed. The data collection framework of tourist attractions based on UAV low-altitude remote sensing is designed, the ecological health evaluation index system of tourist attractions is constructed, and information entropy and analytic hierarchy process were used to determine the combination weight. The gradient boosting decision tree algorithm is used to calculate the ecological health of tourist attractions, and multiple support vector machines are used to construct multi-classifiers to achieve ecological health evaluation. The experimental results show that the average data acquisition time of the method in this paper is 0.76 s, the error rate of the index weight calculation is between -1% and 2%, and the average evaluation accuracy rate is 97.2%.\",\"PeriodicalId\":13984,\"journal\":{\"name\":\"International Journal of Environmental Technology and Management\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.5000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Environmental Technology and Management\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1504/ijetm.2023.134325\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENGINEERING, ENVIRONMENTAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Environmental Technology and Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1504/ijetm.2023.134325","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, ENVIRONMENTAL","Score":null,"Total":0}
引用次数: 0

摘要

针对传统评价方法中存在的旅游地生态健康评价准确率低等问题,提出了一种基于梯度增强决策树的旅游地生态健康评价方法。设计了基于无人机低空遥感的旅游景区数据采集框架,构建了旅游景区生态健康评价指标体系,采用信息熵法和层次分析法确定组合权重。利用梯度增强决策树算法计算旅游景区生态健康状况,利用多支持向量机构建多分类器实现旅游景区生态健康评价。实验结果表明,本文方法的平均数据采集时间为0.76 s,指标权重计算错误率在-1% ~ 2%之间,平均评价准确率为97.2%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An ecological health evaluation of tourist attractions based on gradient boosting decision tree
In order to overcome many problems existing in traditional evaluation methods, such as the low accuracy of the evaluation of ecological health of tourist attractions, an ecological health evaluation method of tourist attractions based on gradient boosting decision tree was proposed. The data collection framework of tourist attractions based on UAV low-altitude remote sensing is designed, the ecological health evaluation index system of tourist attractions is constructed, and information entropy and analytic hierarchy process were used to determine the combination weight. The gradient boosting decision tree algorithm is used to calculate the ecological health of tourist attractions, and multiple support vector machines are used to construct multi-classifiers to achieve ecological health evaluation. The experimental results show that the average data acquisition time of the method in this paper is 0.76 s, the error rate of the index weight calculation is between -1% and 2%, and the average evaluation accuracy rate is 97.2%.
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
CiteScore
1.00
自引率
0.00%
发文量
66
期刊介绍: IJETM is a refereed and authoritative source of information in the field of environmental technology and management. Together with its sister publications IJEP and IJGEnvI, it provides a comprehensive coverage of environmental issues. It deals with the shorter-term, covering both engineering/technical and management solutions.
×
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