Comparison of bias-corrected multisatellite precipitation products by deep learning framework-Reference-Cited by-同舟云学术

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Comparison of bias-corrected multisatellite precipitation products by deep learning framework

Published:2023-02 Issue: Volume:116 Page:103177
ISSN:1569-8432
Container-title:International Journal of Applied Earth Observation and Geoinformation
language:en
Short-container-title:International Journal of Applied Earth Observation and Geoinformation

Author:

Le Xuan-Hien,Nguyen Van Linh,Hai Nguyen Duc,Nguyen Giang V.,Jung Sungho,Lee Giha

Funder

Korea Environmental Industry and Technology Institute

Ministry of Environment

Publisher

Elsevier BV

Subject

Management, Monitoring, Policy and Law,Computers in Earth Sciences,Earth-Surface Processes,Global and Planetary Change

Reference84 articles.

1. Abadi, M. et al., 2015. TensorFlow: Large-scale machine learning on heterogeneous distributed systems. ArXiv, abs/1603.04467.

2. Comparison of BIAS correction techniques for GPCC rainfall data in semi-arid climate;Ajaaj;Stochastic Environ. Res. Risk Assess.,2016

3. PERSIANN-CDR: Daily Precipitation Climate Data Record from Multisatellite Observations for Hydrological and Climate Studies;Ashouri;Bull. Am. Meteorol. Soc.,2015

4. Assessing the efficacy of high-resolution satellite-based PERSIANN-CDR precipitation product in simulating streamflow;Ashouri;J. Hydrometeorol.,2016

5. RF-MEP: A novel Random Forest method for merging gridded precipitation products and ground-based measurements;Baez-Villanueva;Remote Sens. Environ.,2020

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