- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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1 Online-Ressource.
- Language
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Englisch
- Bibliographic citation
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A novel RF-CEEMD-LSTM model for predicting water pollution ; volume:13 ; number:1 ; day:28 ; month:11 ; year:2023 ; pages:1-14 ; date:12.2023
Scientific reports ; 13, Heft 1 (28.11.2023), 1-14, 12.2023
- Creator
-
Ruan, Jinlou
Cui, Yang
Song, Yuchen
Mao, Yawei
- Contributor
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SpringerLink (Online service)
- DOI
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10.1038/s41598-023-48409-6
- URN
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urn:nbn:de:101:1-2024021511153937092170
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
15.08.2025, 7:32 AM CEST
Data provider
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Associated
- Ruan, Jinlou
- Cui, Yang
- Song, Yuchen
- Mao, Yawei
- SpringerLink (Online service)
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COMPARATIVE ANALYSIS OF LSTM, RF AND SVM ARCHITECTURES FOR PREDICTING WIND NATURE FOR SMART CITY PLANNING
Predicting LQ45 financial sector indices using RNN-LSTM
Predicting future global temperature and greenhouse gas emissions via LSTM model
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Development of an LSTM-Broadcasting deep-learning framework for regional air pollution forecast improvement
Development of an LSTM broadcasting deep-learning framework for regional air pollution forecast improvement
LSTM water prediction for feedforward control of moulding sand compressibility
Risk analysis of water pollution
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COMPARATIVE ANALYSIS OF LSTM, RF AND SVM ARCHITECTURES FOR PREDICTING WIND NATURE FOR SMART CITY PLANNING
Predicting LQ45 financial sector indices using RNN-LSTM
Predicting future global temperature and greenhouse gas emissions via LSTM model
Predicting COVID-19 cases using bidirectional LSTM on multivariate time series
Predicting Geolocation of Tweets: Using Combination of CNN and BiLSTM
Algae and water pollution
A Regularized LSTM Method for Predicting Remaining Useful Life of Rolling Bearings
Development of an LSTM-Broadcasting deep-learning framework for regional air pollution forecast improvement
Development of an LSTM broadcasting deep-learning framework for regional air pollution forecast improvement
LSTM water prediction for feedforward control of moulding sand compressibility
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