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在“Elsevier电子期刊”中，命中：397条，耗时：0.0739646 秒

在所有数据库中总计命中：5,731条

1.Daily PM_2.5 concentration prediction based on principal component analysis and LSSVM optimized by cuckoo search algorithm

作者：Wei Sun ^{bdsunwei@126.com} ; Jingyi Sun ; ^{sunjingyi0224@126.com}

关键词：PM2.5 ; Concentration forecasting ; PCA ; LSSVM ; CS

刊名：Journal of Environmental Management

出版年：2017

2.Functional time series forecasting with dynamic updating: An application to intraday particulate matter concentration

作者：Han Lin Shang ; ^{hanlin.shang@anu.edu.au}

关键词：Block moving ; Dynamic updating ; Functional principal component regression ; Functional linear regression ; Maximum entropy bootstrap ; VAR

刊名：Econometrics and Statistics

出版年：2017

3.Developing a statistical based approach for predicting local air quality in complex terrain area

作者：L.K. Kwok^a ; Y.F. Lam^a ; ^b ; ^{yunflam@cityu.edu.hk} ; C.-Y. Tam^c

关键词：Hong Kong air quality ; Statistical-dynamical model ; HYSPLIT and WRF ; Air pollution episode prediction ; Complex terrain region

刊名：Atmospheric Pollution Research

出版年：2017

4.Evaluation of Eutrophication Control Through Hypolimnetic Oxygenation

作者：Anne M. Hansen^a ; ^{ahansen@tlaloc.imta.mx} ; Claudia Herná ; ndez-Martí ; nez^b ; Axel Falcó ; n-Rojas^a

关键词：Hypolimnetic oxygenation ; Eutrophication control ; Internal load ; Oxygen demand ; Water and sediment respiration

刊名：Procedia Earth and Planetary Science

出版年：2017

5.A novel hybrid decomposition-and-ensemble model based on CEEMD and GWO for short-term PM_2.5 concentration forecasting

作者：Mingfei Niu^a ; Yufang Wang^a ; Shaolong Sun^b ; ^{sunshl13@lzu.edu.cn" class="auth_mail" title="E-mail the corresponding author} ; Yongwu Li^b

关键词：Complementary ensemble empirical mode decomposition ; Grey wolf optimizer ; Support vector regression ; Hybrid decomposition-ensemble model ; PM2.5 concentration forecasting

刊名：Atmospheric Environment

出版年：2016

6.An in vitro biorelevant gastrointestinal transfer (BioGIT) system for forecasting concentrations in the fasted upper small intestine: Design, implementation, and evaluation

作者：Alexandros Kourentas^a ; Maria Vertzoni^a ; Nick Stavrinoudakis^a ; Alexandros Symillidis^b ; Joachim Brouwers^c ; Patrick Augustijns^c ; Christos Reppas^a ; Mira Symillides^a ; ^{simillidou@pharm.uoa.gr" class="auth_mail" title="E-mail the corresponding author}

关键词：BioGIT ; Gastrointestinal transfer ; Biorelevant evaluation ; Luminal concentrations ; Precipitation ; Weak bases

刊名：European Journal of Pharmaceutical Sciences

出版年：2016

7.Monotonic trends in spatio-temporal distribution and concentration of monsoon precipitation (1901-2002), West Bengal, India

作者：Soumendu Chatterjee^a ; Ansar Khan^b ; ^{khanansargeo@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; Hashem Akbari^c ; Yupeng Wang^c ; ^d

关键词：Monotonic trend ; Shift in concentration ; Monsoon precipitation ; Precipitation concentration index (PCI) ; Precipitation concentration degree (PCD) ; Precipitation concentration period (PCP) ; Fulcrum (centre of gravity) ; Central India (CI)

刊名：Atmospheric Research

出版年：2016

8.A wavelet-linear genetic programming model for sodium (Na⁺) concentration forecasting in rivers

作者：Masoud Ravansalar^a ; ^{m.ravansalar@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; Taher Rajaee^a ; ^{trajaee@qom.ac.ir" class="auth_mail" title="E-mail the corresponding author} ; Mohammad Zounemat-Kermani^b ; ^{zounemat@uk.ac.ir" class="auth_mail" title="E-mail the corresponding author}

关键词：Water quality ; Na+ concentration ; Wavelet&ndash ; linear genetic programming ; Data driven model

刊名：Journal of Hydrology

出版年：2016

9.Research and Application of Improved Gas Concentration Prediction Model Based on Grey Theory and BP Neural Network in Digital Mine

作者：Sirui Zhang^a ; ^{zhangsr1118@126.com" class="auth_mail" title="E-mail the corresponding author} ; Botao Wang^a ; Xueen Li^b ; Hao Chen^a

关键词：digital mine ; grey theory and BP neural network ; gas concentration ; prediction

刊名：Procedia CIRP

出版年：2016

10.Prediction of fatigue stress concentration factor using extreme learning machine

作者：Baoxian Wang^a ; ^b ; Weigang Zhao^a ; ^b ; ^{zhaoweig2002@126.com" class="auth_mail" title="E-mail the corresponding author} ; Yanliang Du^a ; ^b ; Guangyuan Zhang^a ; Yong Yang^b

关键词：Fatigue stress concentration factor ; Material fatigue resistance ; Extreme learning machine ; Prediction

刊名：Computational Materials Science

出版年：2016

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