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Research Article

A time feature modeling and expression method for vague geographic processes

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Article: 2295989 | Received 25 Apr 2023, Accepted 12 Dec 2023, Published online: 28 Dec 2023
 

ABSTRACT

Geographic processes are dynamic, wherein geographical entities and phenomena change over time and are characterized by vagueness. However, most studies on geographic processes employ precise expression methods to describe and express the changing trajectories of geographical entities and phenomena, ignoring vagueness. This strategy fails to facilitate the objective description and expression of the entire process and may affect analysis results. Herein, a time feature expression model of vague geographic processes is established based on the interval type-2 fuzzy set theory to describe and express the vague time characteristics comprising three model stages that reflect the geographic process evolution sequence: production (beginning), development, and extinction (end). Finally, the practicability of the proposed model is verified using two case studies, i.e. the landing process of typhoon ‘Lekima’ and the desert greening process in Xinjiang, China.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Author contributions

Yue Yin contributed to the concept of the current research, formulated the overarching research goals, conducted the experiment, and wrote the manuscript. Yufeng He translated the manuscript and completed the revisions. Yizhe Feng collected data, formatted the manuscript, and corrected any errors observed. Xueying Zhang provided funding and provided professional comments. Yehua Sheng provided funding, directed the entire process, and was responsible for the quality audit of the manuscript.

Data availability statement

All data used in this study are accessible online from https://disc.gsfc.nasa.gov/. The code of this study is openly available on ‘figshare’ at https://figshare.com/s/4c64213b60d747d08d10.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China under Grant numbers 42301533 and 42071364, and the National Key R&D Program of China under grant number 2021YFB3900903.