预测未来海平面上升:利用气候分析的数据驱动方法

Vinay Nagarad Dasavandi Krishnamurthy, S. Degadwala, Dhairya Vyas
{"title":"预测未来海平面上升:利用气候分析的数据驱动方法","authors":"Vinay Nagarad Dasavandi Krishnamurthy, S. Degadwala, Dhairya Vyas","doi":"10.1109/ICECAA58104.2023.10212399","DOIUrl":null,"url":null,"abstract":"This research article presents a data-driven approach for predicting future sea level rise using climate data analysis. By employing advanced statistical techniques and machine learning algorithms, the study establishes correlations between historical climate variables and observed sea level rise. Ensemble modeling techniques are utilized to explore uncertainties and generate multiple simulations, offering a range of potential outcomes. The findings provide valuable insights for policymakers and coastal communities, enabling informed decision-making and the development of effective strategies to address the challenges posed by rising sea levels. Overall, this research contributes to the field of climate science by providing a robust framework for predicting sea level rise and preparing for its impacts in a changing climate.","PeriodicalId":114624,"journal":{"name":"2023 2nd International Conference on Edge Computing and Applications (ICECAA)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Forecasting Future Sea Level Rise: A Data-driven Approach using Climate Analysis\",\"authors\":\"Vinay Nagarad Dasavandi Krishnamurthy, S. Degadwala, Dhairya Vyas\",\"doi\":\"10.1109/ICECAA58104.2023.10212399\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This research article presents a data-driven approach for predicting future sea level rise using climate data analysis. By employing advanced statistical techniques and machine learning algorithms, the study establishes correlations between historical climate variables and observed sea level rise. Ensemble modeling techniques are utilized to explore uncertainties and generate multiple simulations, offering a range of potential outcomes. The findings provide valuable insights for policymakers and coastal communities, enabling informed decision-making and the development of effective strategies to address the challenges posed by rising sea levels. Overall, this research contributes to the field of climate science by providing a robust framework for predicting sea level rise and preparing for its impacts in a changing climate.\",\"PeriodicalId\":114624,\"journal\":{\"name\":\"2023 2nd International Conference on Edge Computing and Applications (ICECAA)\",\"volume\":\"104 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-07-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 2nd International Conference on Edge Computing and Applications (ICECAA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICECAA58104.2023.10212399\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 2nd International Conference on Edge Computing and Applications (ICECAA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICECAA58104.2023.10212399","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

本文提出了一种利用气候数据分析预测未来海平面上升的数据驱动方法。通过采用先进的统计技术和机器学习算法,该研究建立了历史气候变量与观测到的海平面上升之间的相关性。集成建模技术用于探索不确定性并生成多个模拟,提供一系列潜在的结果。这些发现为决策者和沿海社区提供了有价值的见解,有助于制定明智的决策和有效的战略,以应对海平面上升带来的挑战。总的来说,这项研究为预测海平面上升提供了一个强有力的框架,并为海平面上升对气候变化的影响做好准备,从而对气候科学领域做出了贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Forecasting Future Sea Level Rise: A Data-driven Approach using Climate Analysis
This research article presents a data-driven approach for predicting future sea level rise using climate data analysis. By employing advanced statistical techniques and machine learning algorithms, the study establishes correlations between historical climate variables and observed sea level rise. Ensemble modeling techniques are utilized to explore uncertainties and generate multiple simulations, offering a range of potential outcomes. The findings provide valuable insights for policymakers and coastal communities, enabling informed decision-making and the development of effective strategies to address the challenges posed by rising sea levels. Overall, this research contributes to the field of climate science by providing a robust framework for predicting sea level rise and preparing for its impacts in a changing climate.
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
期刊最新文献
Deep Learning based Sentiment Analysis on Images A Comprehensive Analysis on Unconstraint Video Analysis Using Deep Learning Approaches An Intelligent Parking Lot Management System Based on Real-Time License Plate Recognition BLIP-NLP Model for Sentiment Analysis Botnet Attack Detection in IoT Networks using CNN and LSTM
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1