A new cross-domain prediction model of air pollutant concentration based on secure federated learning and optimized LSTM neural network

As air pollution worsens, the fast prediction of air pollutant concentration becomes increasingly important for public health. This paper proposes a new cross-domain prediction model of air pollutant concentration based on federated learning (FL), differential privacy laplace mechanism (DPLA) and lo...

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Veröffentlicht in:	Environmental science and pollution research international Jg. 30; H. 2; S. 5103 - 5125
Hauptverfasser:	Huang, Guangqiu, Zhao, Xixuan, Lu, Qiuqin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2023 Springer Nature B.V
Schlagworte:	air air pollutants Air Pollutants - analysis Air pollution Air Pollution - analysis Algorithms Aquatic Pollution Atmospheric Protection/Air Quality Control/Air Pollution China data collection Domains Earth and Environmental Science Ecotoxicology Environment Environmental Chemistry Environmental Health Environmental Monitoring - methods Environmental protection Environmental science Federated learning Learning Machine learning Mathematical models memory Meteorological data Neural networks Neural Networks, Computer Outdoor air quality Parameters Passeriformes Pollutants Pollution abatement pollution control Pollution prevention prediction Prediction models Public health Research Article Rivers Search algorithms Short term memory Waste Water Technology Water Management Water Pollution Control China Sparrow search algorithm (SSA) Differential privacy laplace mechanism (DPLA) Air pollutant concentration prediction; Federated learning (FL) LSTM neural network Joint prevention and control
ISSN:	0944-1344, 1614-7499, 1614-7499
Online-Zugang:	Volltext
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