Estimation of river water depth using UAV-assisted RGB imagery and multiple linear regression analysis

Hyeon Tae Moon, Jung Hwan Lee, Ji Moon Yuk, Young Il Moon

Research output: Contribution to journalArticlepeer-review


River cross-section measurement data is one of the most important input data in research related to hydraulic and hydrological modeling, such as flow calculation and flood forecasting warning methods for river management. However, the acquisition of accurate and continuous cross-section data of rivers leading to irregular geometric structure has significant limitations in terms of time and cost. In this regard, a primary objective of this study is to develop a methodology that is able to measure the spatial distribution of continuous river characteristics by minimizing the input of time, cost, and manpower. Therefore, in this study, we tried to examine the possibility and accuracy of continuous cross-section estimation by estimating the water depth for each cross-section through multiple linear regression analysis using RGB-based aerial images and actual data. As a result of comparing with the actual data, it was confirmed that the depth can be accurately estimated within about 2 m of water depth, which can capture spatially heterogeneous relationships, and this is expected to contribute to accurate and continuous river cross-section acquisition.

Original languageEnglish
Pages (from-to)1059-1070
Number of pages12
JournalJournal of Korea Water Resources Association
Issue number12
StatePublished - Dec 2020


  • Multiple linear regression
  • UAV
  • Water depth estimation


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