Spatially Downscaling IMERG at Daily Scale Using Machine Learning Approaches Over Zhejiang, Southeastern China
Precipitation estimates with high accuracy and fine spatial resolution play an important role in the field of meteorology, hydrology, and ecology. In this study, support vector machine (SVM) and back-propagation neural network (BPNN) machine learning algorithms were used to downscale the Integrated...
Main Authors: | Xiaoxiao Min, Ziqiang Ma, Jintao Xu, Kang He, Zhige Wang, Qingliang Huang, Jun Li |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2020-06-01
|
Series: | Frontiers in Earth Science |
Subjects: | |
Online Access: | https://www.frontiersin.org/article/10.3389/feart.2020.00146/full |
Similar Items
-
Comparisons of Spatially Downscaling TMPA and IMERG over the Tibetan Plateau
by: Ziqiang Ma, et al.
Published: (2018-11-01) -
The First Comparisons of IMERG and the Downscaled Results Based on IMERG in Hydrological Utility over the Ganjiang River Basin
by: Ziqiang Ma, et al.
Published: (2018-10-01) -
A New HASM-Based Downscaling Method for High-Resolution Precipitation Estimates
by: Na Zhao, et al.
Published: (2021-07-01) -
An Efficient Downscaling Scheme for High-Resolution Precipitation Estimates over a High Mountainous Watershed
by: Na Zhao
Published: (2021-01-01) -
Comparison of Different Methods for Spatial Downscaling of GPM IMERG V06B Satellite Precipitation Product Over a Typical Arid to Semi-Arid Area
by: Cheng Chen, et al.
Published: (2020-11-01)