Analysis of Mixing Patterns of River Confluences through 3D Spatial Interpolation of Sensor Measurement Data

被引:2
|
作者
Lee, Chang Hyun [1 ]
Kim, Kyung Dong [2 ]
Lyu, Siwan [3 ]
Kim, Dong Su [2 ]
Kim, Young Do [1 ]
机构
[1] Myongji Univ, Dept Civil & Environm Engn, Yongin 17058, South Korea
[2] Dankook Univ, Dept Civil Engn, Yongin 16890, South Korea
[3] Changwon Natl Univ, Dept Civil Engn, Chang Won 51140, South Korea
关键词
Kriging technique; river analysis; hydrology; river data extrapolation; inverse distance weighting; 3D interpolation;
D O I
10.3390/w15050925
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Aquatic environmental problems, such as algae, turbid water, and poor oxygen content, have become increasingly common. In river analysis, hydrological and water quality characteristics are used for evaluating aquatic ecological health, which necessitates continuous monitoring. In addition, because measurements are conducted using a fixed measurement method, the hydrological and water quality characteristics are not investigated for the entire river. Furthermore, obtaining high-resolution data is tedious, and the measurement area and time are limited. Hence, low-resolution data acquisition is generally preferred; however, this requires an appropriate interpolation method to obtain a wide range of data. Therefore, a 3D interpolation method for river data is proposed herein. The overall hydraulic and water quality information of a river is presented by visualizing the low-resolution measurements using spatial interpolation. The Kriging technique was applied to the river mapping to improve the mapping precision through data visualization and quantitative evaluation.
引用
收藏
页数:18
相关论文
共 50 条
  • [21] 3D micro profile measurement with the method of spatial frequency domain analysis
    Xu, Yongxiang
    AOPC 2015: OPTICAL TEST, MEASUREMENT, AND EQUIPMENT, 2015, 9677
  • [22] Error Analysis of 3D Simplex Interpolation Method
    Luo, Wen
    Liu, Jinbo
    Li, Zengrui
    Song, Jiming
    2020 IEEE INTERNATIONAL SYMPOSIUM ON ANTENNAS AND PROPAGATION AND NORTH AMERICAN RADIO SCIENCE MEETING, 2020, : 2063 - 2064
  • [23] 3D fluorescence spectral data interpolation by using IDW
    He, Qinghang
    Zhang, Zhenxi
    Yi, Chao
    SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY, 2008, 71 (03) : 743 - 745
  • [24] Interpolation of Scattered 3D PTV Data to a Regular Grid
    Heinrich Stüer
    Stefan Blaser
    Flow, Turbulence and Combustion, 2000, 64 : 215 - 232
  • [25] An efficient interpolation approach for insufficient 3D field data
    Kim, Bona
    Jeong, Soocheol
    Byun, Joongmoo
    Kim, Young
    EXPLORATION GEOPHYSICS, 2018, 49 (01) : 58 - 67
  • [26] Interpolation of scattered 3D PTV data to a regular grid
    Stüer, H
    Blaser, S
    FLOW TURBULENCE AND COMBUSTION, 2000, 64 (03) : 215 - 232
  • [27] 3d visualisation in spatial data infrastructures
    Heinen, T
    May, M
    Schmidt, B
    SMART GRAPHICS, PROCEEDINGS, 2005, 3638 : 222 - 229
  • [28] The Analysis of Influence of 3D Modeling Quality on Body Measurement Data
    Shang, Xiao-Mei
    Zhou, Qi-Hui
    Wang, Xue-Wei
    TEXTILE BIOENGINEERING AND INFORMATICS SYMPOSIUM PROCEEDINGS, VOLS 1-3, 2011, : 131 - 135
  • [29] Spatial and temporal analysis of DIII-D 3D magnetic diagnostic data
    Strait, E. J.
    King, J. D.
    Hanson, J. M.
    Logan, N. C.
    REVIEW OF SCIENTIFIC INSTRUMENTS, 2016, 87 (11):
  • [30] Head 3D Motion Measurement by Read Sensor
    Yuan, Zhimin
    Ong, Chun Lian
    Santoso, Budi
    Ang, Shiming
    Wang, Hongtao
    2018 ASIA-PACIFIC MAGNETIC RECORDING CONFERENCE (APMRC), 2018,