Self-Powered Wireless Sensor Matrix for Air Pollution Detection with a Neural Predictor
Energies
; 15(6):1962, 2022.
Article
in English
| ProQuest Central | ID: covidwho-1760457
ABSTRACT
Predicting the status of particulate air pollution is extremely important in terms of preventing possible vascular and lung diseases, improving people’s quality of life and, of course, actively counteracting pollution magnification. Hence, there is great interest in developing methods for pollution prediction. In recent years, the importance of methods based on classical and more advanced neural networks is increasing. However, it is not so simple to determine a good and universal method due to the complexity and multiplicity of measurement data. This paper presents an approach based on Deep Learning networks, which does not use Bayesian sub-predictors. These sub-predictors are used to marginalize the importance of some data part from multisensory platforms. In other words—to filter out noise and mismeasurements before the actual processing with neural networks. The presented results shows the applied data feature extraction method, which is embedded in the proposed algorithm, allows for such feature clustering. It allows for more effective prediction of future air pollution levels (accuracy—92.13%). The prediction results shows that, besides using standard measurements of temperature, humidity, wind parameters and illumination, it is possible to improve the performance of the predictor by including the measurement of traffic noise (Accuracy—94.61%).
Energy; air pollution; deep learning; prediction; wireless sensor; Feature extraction; Severe acute respiratory syndrome coronavirus 2; Communication; Aerosols; Social exclusion; Energy storage; Pollution detection; Machine learning; Transportation noise; Pollution levels; Wireless networks; Embedded systems; Bayesian analysis; Neural networks; Research centers; Noise measurement; Predictions; Power; Clustering; Sensors; Lung diseases; Quality of life; Algorithms; Methods; Information processing; Environmental protection; United States--US
Full text:
Available
Collection:
Databases of international organizations
Database:
ProQuest Central
Type of study:
Prognostic study
Language:
English
Journal:
Energies
Year:
2022
Document Type:
Article
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