2022
DOI: 10.1016/j.cageo.2022.105097
|View full text |Cite
|
Sign up to set email alerts
|

Hydrologic similarity based on width function and hypsometry: An unsupervised learning approach

Help me understand this report
View preprint versions

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
0
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
5
1

Relationship

0
6

Authors

Journals

citations
Cited by 10 publications
(1 citation statement)
references
References 108 publications
0
0
0
Order By: Relevance
“…Previous studies commonly employed the 3 rd order polynomials to fit hypsometric curves and then derive hypsometric attributes based on the polynomial function (Harlin, 1978;Luo, (b) (c) 2000). However, it has been shown that the 3 rd order polynomials may not accurately capture the hypsometric curve's intricate shape, and various alternative formulae have been proposed (Bajracharya & Jain, 2022;Liffner et al, 2018;Vanderwaal & Ssegane, 2013). Here, we adopted a 9 th order polynomial function to fit the hypsometric curve, and the five statistical attributes were obtained analytically from the fitted function, thus avoiding the potential numerical errors associated with directly using unsmoothed discrete data.…”
Section: Extraction Of Hypsometric Attributesmentioning
confidence: 99%
“…Previous studies commonly employed the 3 rd order polynomials to fit hypsometric curves and then derive hypsometric attributes based on the polynomial function (Harlin, 1978;Luo, (b) (c) 2000). However, it has been shown that the 3 rd order polynomials may not accurately capture the hypsometric curve's intricate shape, and various alternative formulae have been proposed (Bajracharya & Jain, 2022;Liffner et al, 2018;Vanderwaal & Ssegane, 2013). Here, we adopted a 9 th order polynomial function to fit the hypsometric curve, and the five statistical attributes were obtained analytically from the fitted function, thus avoiding the potential numerical errors associated with directly using unsmoothed discrete data.…”
Section: Extraction Of Hypsometric Attributesmentioning
confidence: 99%