Application of Random Forest Algorithm for Merging Multiple Satellite Precipitation Products across South Korea

Precipitation is a crucial component of the water cycle and plays a key role in hydrological processes. Recently, satellite-based precipitation products (SPPs) have provided grid-based precipitation with spatiotemporal variability. However, SPPs contain a lot of uncertainty in estimated precipitatio...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2021-10, Vol.13 (20), p.4033
Main Authors: Nguyen, Giang V., Le, Xuan-Hien, Van, Linh Nguyen, Jung, Sungho, Yeon, Minho, Lee, Giha
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Hydrologic cycle
Hydrologic data
Hydrology
Learning algorithms
Machine learning
merging
Methods
Precipitation
Rain
Rainfall
Rainfall measurement
random forest
Remote sensing
South Korea
Spatial discrimination
Spatial resolution
Statistical methods
Statistics
Topography
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Summary:Precipitation is a crucial component of the water cycle and plays a key role in hydrological processes. Recently, satellite-based precipitation products (SPPs) have provided grid-based precipitation with spatiotemporal variability. However, SPPs contain a lot of uncertainty in estimated precipitation, and the spatial resolution of these products is still relatively coarse. To overcome these limitations, this study aims to generate new grid-based daily precipitation based on a combination of rainfall observation data with multiple SPPs for the period of 2003–2017 across South Korea. A Random Forest (RF) machine-learning algorithm model was applied for producing a new merged precipitation product. In addition, several statistical linear merging methods have been adopted to compare with the results achieved from the RF model. To investigate the efficiency of RF, rainfall data from 64 observed Automated Synoptic Observation System (ASOS) installations were collected to analyze the accuracy of products through several continuous as well as categorical indicators. The new precipitation values produced by the merging procedure generally not only report higher accuracy than a single satellite rainfall product but also indicate that RF is more effective than the statistical merging method. Thus, the achievements from this study point out that the RF model might be applied for merging multiple satellite precipitation products, especially in sparse region areas.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs13204033