中文版 | English

在结果中检索

GSTDTAP

浏览/检索结果: 共3条，第1-3条 帮助

限定条件        
已选(0)清除 条数/页：   排序方式：
Integrating precipitation zoning with random forest regression for the spatial downscaling of satellite-based precipitation: A case study of the Lancang-Mekong River basin 期刊论文
INTERNATIONAL JOURNAL OF CLIMATOLOGY, 2019, 39 (10) : 3947-3961
作者:  Zhang, Jing;  Fan, Hui;  He, Daming;  Chen, Jiwei
收藏  |  浏览/下载：7/0  |  提交时间：2019/11/27
lancang-mekong river basin  precipitation zoning  random forest regression  REOF analysis  spatial downscaling  
High-resolution mapping of daily climate variables by aggregating multiple spatial data sets with the random forest algorithm over the conterminous United States 期刊论文
INTERNATIONAL JOURNAL OF CLIMATOLOGY, 2019, 39 (6) : 2964-2983
作者:  Hashimoto, Hirofumi;  Wang, Weile;  Melton, Forrest S.;  Moreno, Adam L.;  Ganguly, Sangram;  Michaelis, Andrew R.;  Nemani, Ramakrishna R.
收藏  |  浏览/下载：10/0  |  提交时间：2019/11/26
daily surface climate  machine learning  NEX-GDM  precipitation  random forest  solar radiation and wind speed  temperature  
Using multi-model ensembles of CMIP5 global climate models to reproduce observed monthly rainfall and temperature with machine learning methods in Australia 期刊论文
INTERNATIONAL JOURNAL OF CLIMATOLOGY, 2018, 38 (13) : 4891-4902
作者:  Wang, Bin;  Zheng, Lihong;  Liu, De Li;  Ji, Fei;  Clark, Anthony;  Yu, Qiang
收藏  |  浏览/下载：5/0  |  提交时间：2019/04/09
GCMs  machine learning  multi-model ensemble  random forest  support vector machine