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}
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.