A flood knowledge-constrained large language model interactable with GIS: enhancing public risk perception of floods

IF 4.3 1区地球科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

International Journal of Geographical Information Science Pub Date : 2024-02-06 DOI:10.1080/13658816.2024.2306167

Jun Zhu, Pei Dang, Yungang Cao, Jianbo Lai, Yukun Guo, Ping Wang, Weilian Li

引用次数: 0

Abstract

Public’s rational flood mitigation behaviors depend on accurate perception of flood risks. The use of natural language for flood risk perception is an effective approach, and it is critical to ensu...

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可与地理信息系统交互的受洪水知识制约的大型语言模型：增强公众对洪水的风险认知

公众的理性防洪减灾行为取决于对洪水风险的准确认知。使用自然语言进行洪水风险感知是一种有效的方法，而确保洪水风险感知的准确性则至关重要。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Geographical Information Science 地学-计算机：信息系统

CiteScore

11.00

自引率

7.00%

发文量

审稿时长

9 months

期刊介绍： International Journal of Geographical Information Science provides a forum for the exchange of original ideas, approaches, methods and experiences in the rapidly growing field of geographical information science (GIScience). It is intended to interest those who research fundamental and computational issues of geographic information, as well as issues related to the design, implementation and use of geographical information for monitoring, prediction and decision making. Published research covers innovations in GIScience and novel applications of GIScience in natural resources, social systems and the built environment, as well as relevant developments in computer science, cartography, surveying, geography and engineering in both developed and developing countries.