中文版 | English

在结果中检索

GSTDTAP

浏览/检索结果: 共5条，第1-5条 帮助

限定条件        
已选(0)清除 条数/页：   排序方式：
A review of flexibility of residential electricity demand as climate solution in four EU countries 期刊论文
ENVIRONMENTAL RESEARCH LETTERS, 2020, 15 (7)
作者:  Mata, Erika;  Ottosson, Jonas;  Nilsson, Johanna
收藏  |  浏览/下载：18/0  |  提交时间：2020/08/18
demand side management  flexibility  grid edge  residential buildings  mitigation potentials  electricity demand  
Monitoring hydropower reliability in Malawi with satellite data and machine learning 期刊论文
ENVIRONMENTAL RESEARCH LETTERS, 2020, 15 (1)
作者:  Falchetta, Giacomo;  Kasamba, Chisomo;  Parkinson, Simon C.
收藏  |  浏览/下载：8/0  |  提交时间：2020/07/02
hydroelectricity  vulnerability  extreme hydroclimatic events  energy-climate-water nexus  random forests  remote sensing  
How can Big Data and machine learning benefit environment and water management: a survey of methods, applications, and future directions 期刊论文
ENVIRONMENTAL RESEARCH LETTERS, 2019, 14 (7)
作者:  Sun, Alexander Y.;  Scanlon, Bridget R.
收藏  |  浏览/下载：8/0  |  提交时间：2019/11/27
machine learning  deep learning  predictive analytics  artificial intelligence  environmental management  big Data  remote sensing  
Modeling spatial climate change landuse adaptation with multi-objective genetic algorithms to improve resilience for rice yield and species richness and to mitigate disaster risk 期刊论文
ENVIRONMENTAL RESEARCH LETTERS, 2019, 14 (2)
作者:  Yoon, Eun Joo;  Thorne, James H.;  Park, Chan;  Lee, Dong Kun;  Kim, Kwang Soo;  Yoon, Heeyeun;  Seo, Changwan;  Lim, Chul-Hee;  Kim, Haeryung;  Song, Young-Il
收藏  |  浏览/下载：7/0  |  提交时间：2019/04/09
scenario planning  landslides  economic value  landuse conversion  trade-offs  South Korea  
Machine learning to analyze the social-ecological impacts of natural resource policy: insights from community forest management in the Indian Himalaya 期刊论文
ENVIRONMENTAL RESEARCH LETTERS, 2019, 14 (2)
作者:  Rana, Pushpendra;  Miller, Daniel C.
收藏  |  浏览/下载：5/0  |  提交时间：2019/04/09
forest policy  community forest management  forest livelihoods  deforestation  machine learning  impact evaluation