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1 Ergebnisse
1
Development of Predictive Models for "Very Poor" Beach Wate..:
Jiuhao Guo (11548362)
;
Joseph H. W. Lee (9597232)
https://figshare.com/articles/journal_contribution/Development_of_Predictive_Models_for_Very_Poor_Beach_Water_Quality_Gradings_Using_Class-Imbalance_Learning/16786353. , 2021
Link:
https://doi.org/10.1021/acs.est.1c03350.s001
RT Journal T1
Development of Predictive Models for "Very Poor" Beach Water Quality Gradings Using Class-Imbalance Learning
UL https://suche.suub.uni-bremen.de/peid=base-ftsmithonian:oai:figshare.com:article_16786353&Exemplar=1&LAN=DE A1 Jiuhao Guo (11548362) A1 Joseph H. W. Lee (9597232) YR 2021 K1 Ecology K1 Science Policy K1 Space Science K1 Environmental Sciences not elsewhere classified K1 Biological Sciences not elsewhere classified K1 Information Systems not elsewhere classified K1 water quality criterion K1 unresolved challenging issue K1 protect public health K1 poor " occasions K1 pollution characteristics using K1 multiple linear regression K1 predicting rare events K1 field data available K1 binary classification modeling K1 predicting bacterial concentrations K1 high bacterial concentrations K1 significantly outperforms mlr K1 proposed method results K1 three marine beaches K1 model using class K1 proposed class K1 marine environments K1 infrequent events K1 imbalance method K1 data comparison K1 classification tree K1 wide range K1 useful tools K1 significant improvement K1 magnitude less K1 imbalance learning K1 hong kong K1 four gradings K1 different hydrographic K1 conditions shows K1 beach management K1 artificial intelligence K1 100 ml JF https://figshare.com/articles/journal_contribution/Development_of_Predictive_Models_for_Very_Poor_Beach_Water_Quality_Gradings_Using_Class-Imbalance_Learning/16786353 LK http://dx.doi.org/https://doi.org/10.1021/acs.est.1c03350.s001 DO https://doi.org/10.1021/acs.est.1c03350.s001 SF ELIB - SuUB Bremen
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