Chrome Extension
WeChat Mini Program
Use on ChatGLM

Challenges in and Opportunities for International Collaboration: Costing Flood Damages and Losses Across Canada, Mexico, and the United States

BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY(2023)

Simon Fraser Univ | Univ Arizona | Commiss Environm Cooperat | Ctr Nacl Prevenc Desastres | Univ Oklahoma | Carleton Univ

Cited 1|Views26
Abstract
Flooding, including inland and coastal flooding, is one of the most devastating and costly natural hazards in Canada, Mexico, and the United States. Recent research conducted by an international team has focused on understanding the true and comprehensive economic costs of floods, with an eye toward addressing their impacts, allocating adequate resources for monitoring and preparedness, and building resilient communities. Flood-costing methods vary greatly among federal and subnational jurisdictions across the three North American countries. Because the rigor and consistency of existing datasets across the three countries vary significantly, it is also difficult to determine the economic impacts of cross-border events. This paper aims to critically analyze the research methods used to conduct this trinational project and develop recommendations for enhancing impacts of such work in the future. We discuss three major research barriers: gaps in knowledge and research capacity, differences in data collation and analysis methods across the three countries, and linguistic barriers in working across disciplines and economic sectors. We also explore how the COVID-19 pandemic significantly added to these three barriers. We propose creation of new institutional mechanisms that can play a major role in developing comprehensive, consistent, and cohesive data gathering approaches in Canada, Mexico, and the United States.
More
Translated text
Key words
North America,Databases,Damage assessment,Emergency preparedness,Flood events,Policy
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Related Papers
Ruth Abegaz,Fei Wang, Jun Xu
2024

被引用0 | 浏览

Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper

要点】:本论文分析了加拿大、美国和墨西哥三国合作计算洪水损失的挑战与机遇,并提出未来改进建议。

方法】:论文采用批判性分析法对跨国洪水损失研究方法进行评估,并提出增强未来研究影响的建议。

实验】:研究遇到了三大障碍:知识与研究能力缺口、三国间数据收集和分析方法的差异以及跨学科和部门合作中的语言障碍;此外,新冠疫情加剧了这些障碍。论文建议建立新的机构机制,以开发三国间全面、一致和协调的数据收集方法。