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Incorporating machine learning with biophysical model can improve the evaluation of climate extremes impacts on wheat yield in south-eastern Australia 期刊论文
AGRICULTURAL AND FOREST METEOROLOGY, 2019, 卷号: 275, 页码: 100-113
作者:  Feng, Puyu;  Wang, Bin;  Liu, De Li;  Waters, Cathy;  Yu, Qiang
收藏  |  浏览/下载：37/0  |  提交时间：2019/10/08
Extreme climate events  Wheat yield  APSIM  Random forest  Hybrid model  
Hydrological Responses to the Future Climate Change in a Data Scarce Region, Northwest China: Application of Machine Learning Models 期刊论文
WATER, 2019, 卷号: 11, 期号: 8
作者:  Zhu, Rui;  Yang, Linshan;  Liu, Tao;  Wen, Xiaohu;  Zhang, Liming;  Chang, Yabin
收藏  |  浏览/下载：31/0  |  提交时间：2019/10/08
global climate model  hydrological response  extreme learning machine  support vector regression  Heihe River  
Intercomparison of machine learning methods for statistical downscaling: the case of daily and extreme precipitation 期刊论文
THEORETICAL AND APPLIED CLIMATOLOGY, 2019, 卷号: 137, 期号: 1-2, 页码: 557-570
作者:  Vandal, Thomas;  Kodra, Evan;  Ganguly, Auroop R.
收藏  |  浏览/下载：8/0  |  提交时间：2019/10/08
Modelling Betula utilis distribution in response to climate-warming scenarios in Hindu-Kush Himalaya using random forest 期刊论文
BIODIVERSITY AND CONSERVATION, 2019, 卷号: 28, 期号: 8-9, 页码: 2295-2317
作者:  Mohapatra, Jakesh;  Singh, Chandra Prakash;  Hamid, Maroof;  Verma, Anirudh;  Semwal, Sudeep Chandra;  Gajmer, Bandan;  Khuroo, Anzar A.;  Kumar, Amit;  Nautiyal, Mohan C.;  Sharma, Narpati;  Pandya, Himanshu A.
收藏  |  浏览/下载：20/0  |  提交时间：2019/10/08
Alpine treeline ecotone  Climate change  Elevation  Machine learning  Niche modelling  
Agricultural land suitability analysis: State-of-the-art and outlooks for integration of climate change analysis 期刊论文
AGRICULTURAL SYSTEMS, 2019, 卷号: 173, 页码: 172-208
作者:  Akpoti, Komlavi;  Kabo-bah, Amos T.;  Zwart, Sander J.
收藏  |  浏览/下载：14/0  |  提交时间：2019/10/08
Agriculture  Land suitability analysis  Climate change  Multi-criteria evaluation  Machine learning  Predictors  
Estimating the daily pollen concentration in the atmosphere using machine learning and NEXRAD weather radar data 期刊论文
ENVIRONMENTAL MONITORING AND ASSESSMENT, 2019, 卷号: 191, 期号: 7
作者:  Zewdie, Gebreab K.;  Lary, David J.;  Liu, Xun;  Wu, Daji;  Levetin, Estelle
收藏  |  浏览/下载：33/0  |  提交时间：2019/10/08
Pollen  NEXRAD  Weather  Machine learning  Neural network  Random forest  Environmental public health  
Agglomerative Clustering of Enteric Infections and Weather Parameters to Identify Seasonal Outbreaks in Cold Climates 期刊论文
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 2019, 卷号: 16, 期号: 12
作者:  Stashevsky, Pavel S.;  Yakovina, Irina N.;  Alarcon Falconi, Tania M.;  Naumova, Elena N.
收藏  |  浏览/下载：31/0  |  提交时间：2019/10/08
machine learning  agglomerative clustering  t-SNE method  harmonic regression models  salmonellosis  non-specific enteric infections  seasonality  meteorological parameters  climate change  
Stepwise extreme learning machine for statistical downscaling of daily maximum and minimum temperature 期刊论文
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT, 2019, 卷号: 33, 期号: 4-6, 页码: 1035-1056
作者:  MoradiKhaneghahi, Mahsa;  Lee, Taesam;  Singh, Vijay P.
收藏  |  浏览/下载：28/0  |  提交时间：2019/10/08
Artificial neural network  Extreme learning machine  Feature selection  Stepwise  Temperature downscaling  
Hourly gridded air temperatures of South Africa derived from MSG SEVIRI 期刊论文
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2019, 卷号: 78, 页码: 261-267
作者:  Meyer, Hanna;  Schmidt, Johannes;  Detsch, Florian;  Nauss, Thomas
收藏  |  浏览/下载：14/0  |  提交时间：2019/10/08
Air temperature  Climate  Machine leaming  Meteosat  Random Forest  South Africa  
Frontiers in data analytics for adaptation research: Topic modeling 期刊论文
WILEY INTERDISCIPLINARY REVIEWS-CLIMATE CHANGE, 2019, 卷号: 10, 期号: 3
作者:  Lesnikowski, Alexandra;  Belfer, Ella;  Rodman, Emma;  Smith, Julie;  Biesbroek, Robbert;  Wilkerson, John D.;  Ford, James D.;  Berrang-Ford, Lea
收藏  |  浏览/下载：11/0  |  提交时间：2019/10/08
climate change adaptation  governance  policy  quantitative text analysis  topic models