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
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Zusammenfassung: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 long and short-term memory network optimized by sparrow search algorithm (SSA-LSTM), named FL-DPLA-SSA-LSTM. Firstly, with FL, SSA-LSTM is used as local training model for each city and predicts air pollutant concentration. Secondly, DPLA is used to add noise to the local model parameters, which can protect local data security. Then, the global model is updated by using the federated averaging algorithm (FedAvg). Lastly, FL is used to share global model for all cities, which can safely and quickly cross-domain predict air pollutant concentration. For data set, it is taken from hourly air pollutants and meteorological data from 12 cities in the Fenhe River and Weihe River Plains in China in 2020. The experimental results show that the prediction performance of the proposed model is significantly better than all comparison models. FedAvg updating with local model parameters with DPLA noise has little effect on the performance of the global model and even exceeds that of the global model. The calculation time of FL-DPLA-SSA-LSTM model is reduced by 99.95% compared with that of not using FL-DPLA machine learning model. It is proved that the model is high sharing and high safety, which greatly improves the training efficiency and has better generalization ability. It is significant for joint air pollution prevention and control and environmental protection.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0944-1344
1614-7499
1614-7499
DOI:10.1007/s11356-022-22454-6