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Evaluation of deep learning algorithm for inflow forecasting: a case study of Durian Tunggal Reservoir, Peninsular Malaysia 期刊论文
Natural Hazards, 2021
作者:  Latif S.D.;  Ahmed A.N.;  Sathiamurthy E.;  Huang Y.F.;  El-Shafie A.
收藏  |  浏览/下载：34/0  |  提交时间：2021/09/01
Artificial neural network (ANN)  Inflow prediction model  Long short-term memory (LSTM)  Malaysia  Support vector machine (SVM)  Water resources management  
Prediction of droughts over Pakistan using machine learning algorithms 期刊论文
, 2020, 卷号: 139
作者:  Khan N.;  Sachindra D.A.;  Shahid S.;  Ahmed K.;  Shiru M.S.;  Nawaz N.
收藏  |  浏览/下载：39/0  |  提交时间：2020/07/28
Atmospheric humidity  Climate change  Drought  Forecasting  Learning algorithms  Nearest neighbor search  Neural networks  Support vector machines  Wind  Atmospheric research  K nearest neighbours (k-NN)  K-nearest neighbours  National centres for environmental predictions  Pakistan  Recursive feature elimination  Temporal and spatial  Vulnerable communities  Learning systems  algorithm  artificial neural network  climate prediction  drought  machine learning  numerical model  precipitation (climatology)  Arabian Sea  Caspian Sea  Indian Ocean  Pakistan  
PoreFlow-Net: A 3D convolutional neural network to predict fluid flow through porous media 期刊论文
, 2020, 卷号: 138
作者:  Santos J.E.;  Xu D.;  Jo H.;  Landry C.J.;  Prodanović M.;  Pyrcz M.J.
收藏  |  浏览/下载：27/0  |  提交时间：2020/07/28
Binary images  Convolution  Deep learning  Deep neural networks  Flow fields  Flow of fluids  Forecasting  Learning systems  Mechanical permeability  Network architecture  Porous materials  Velocity  Disruptive technology  Fluid velocity field  Geometrical informations  Machine learning models  Orders of magnitude  Spatial relationships  Subsurface formations  Surrogate model  Convolutional neural networks  artificial neural network  digital image  flow modeling  fluid flow  permeability  porous medium  prediction  rock mechanics  surrogate method  three-dimensional modeling  
Technical note: Deep learning for creating surrogate models of precipitation in Earth system models 期刊论文
, 2020, 卷号: 20, 期号: 4
作者:  Weber T.;  Corotan A.;  Hutchinson B.;  Kravitz B.;  Link R.
收藏  |  浏览/下载：17/0  |  提交时间：2020/07/28
amplification  artificial neural network  climate prediction  machine learning  numerical model  optimization  precipitation assessment  sampling  
Global river water warming due to climate change and anthropogenic heat emission 期刊论文
GLOBAL AND PLANETARY CHANGE, 2020, 卷号: 193
作者:  Liu, Shuang;  Xie, Zhenghui;  Liu, Bin;  Wang, Yan;  Gao, Junqiang;  Zeng, Yujin;  Xie, Jinbo;  Xie, Zhipeng;  Jia, Binghao;  Qin, Peihua;  Li, Ruichao;  Wang, Longhuan;  Chen, Si
收藏  |  浏览/下载：33/0  |  提交时间：2022/06/21
ARTIFICIAL NEURAL-NETWORK  EARTH SYSTEM MODEL  THERMAL REGIME  LAND-SURFACE  TEMPERATURE  HYDROLOGY  DYNAMICS  STREAM  
Assessing the effects of climate change on water quality of plateau deep-water lake - A study case of Hongfeng Lake 期刊论文
SCIENCE OF THE TOTAL ENVIRONMENT, 2019, 卷号: 647, 页码: 1518-1530
作者:  Longyang, Qianqiu
收藏  |  浏览/下载：30/0  |  提交时间：2019/10/08
Climate change  Artificial neural network model  Deep-water lake  Eutrophication  Non-point pollution  
Mineralogical composition and total organic carbon quantification using X-ray fluorescence data from the Upper Cretaceous Eagle Ford Group in southern Texas 期刊论文
, 2019, 卷号: 103, 期号: 12
作者:  Alnahwi A.;  Loucks R.G.
收藏  |  浏览/下载：43/0  |  提交时间：2020/07/28
Fluorescence  Infill drilling  Minerals  X ray diffraction  Mineralogical compositions  Point measurement  Relative abundance  Sample preparation  Semiquantitative model  Total Organic Carbon  Training process  X ray fluorescence  Organic carbon  artificial neural network  chemical composition  Cretaceous  Internet  machine learning  organic matter  quantitative analysis  total organic carbon  X-ray fluorescence  Texas  United States  
A deep learning algorithm to estimate hourly global solar radiation from geostationary satellite data 期刊论文
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2019, 卷号: 114
作者:  Jiang, Hou;  Lu, Ning;  Qin, Jun;  Tang, Wenjun;  Yao, Ling
收藏  |  浏览/下载：19/0  |  提交时间：2022/06/21
ARTIFICIAL NEURAL-NETWORK  INTELLIGENCE TECHNIQUES  IRRADIANCE  ENERGY  MODEL  PREDICTION