An IoT-Based Air Quality Monitoring with Deep Learning Model System

2021 ◽  
pp. 643-650
Author(s):  
Harshit Srivastava ◽  
Kailash Bansal ◽  
Santos Kumar Das ◽  
Santanu Sarkar
Keyword(s):  
Deep Learning ◽  
Air Quality ◽  
Learning Model ◽  
Model System ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Deep Learning Model

scholarly journals Optimising Deep Learning at the Edge for Accurate Hourly Air Quality Prediction

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1064
Author(s):  
I Nyoman Kusuma Wardana ◽  
Julian W. Gardner ◽  
Suhaib A. Fahmy
Keyword(s):  
Deep Learning ◽  
Air Quality ◽  
Short Term Memory ◽  
Model Performance ◽  
Learning Model ◽  
Sensor Data ◽  
Raspberry Pi ◽  
Learning Models ◽  
Proposed Model ◽  
Deep Learning Model

Accurate air quality monitoring requires processing of multi-dimensional, multi-location sensor data, which has previously been considered in centralised machine learning models. These are often unsuitable for resource-constrained edge devices. In this article, we address this challenge by: (1) designing a novel hybrid deep learning model for hourly PM2.5 pollutant prediction; (2) optimising the obtained model for edge devices; and (3) examining model performance running on the edge devices in terms of both accuracy and latency. The hybrid deep learning model in this work comprises a 1D Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) to predict hourly PM2.5 concentration. The results show that our proposed model outperforms other deep learning models, evaluated by calculating RMSE and MAE errors. The proposed model was optimised for edge devices, the Raspberry Pi 3 Model B+ (RPi3B+) and Raspberry Pi 4 Model B (RPi4B). This optimised model reduced file size to a quarter of the original, with further size reduction achieved by implementing different post-training quantisation. In total, 8272 hourly samples were continuously fed to the edge device, with the RPi4B executing the model twice as fast as the RPi3B+ in all quantisation modes. Full-integer quantisation produced the lowest execution time, with latencies of 2.19 s and 4.73 s for RPi4B and RPi3B+, respectively.

Air quality monitoring and analysis with dynamic training using deep learning

2020 ◽  
Author(s):  
Endah Kristiani ◽  
Ching-Fang Lee ◽  
Chao-Tung Yang ◽  
Chin-Yin Huang ◽  
Yu-Tse Tsan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Air Quality ◽  
Quality Monitoring ◽  
Air Quality Monitoring

scholarly journals Distributed Deep Features Extraction Model for Air Quality Forecasting

2020 ◽  
Vol 12 (19) ◽  
pp. 8014
Author(s):  
Axel Gedeon Mengara Mengara ◽  
Younghak Kim ◽  
Younghwan Yoo ◽  
Jaehun Ahn
Keyword(s):  
Deep Learning ◽  
Air Quality ◽  
Short Term Memory ◽  
Meteorological Data ◽  
Learning Model ◽  
Training Time ◽  
Metropolitan City ◽  
Pm2.5 And Pm10 ◽  
Deep Learning Model ◽  
Air Quality Forecasting

Several studies in environmental engineering emphasize the importance of air quality forecasting for sustainable development around the world. In this paper, we studied a new approach for air quality forecasting in Busan metropolitan city. We proposed a convolutional Bi-Directional Long-Short Term Memory (Bi-LSTM) autoencoder model trained using a distributed architecture to predict the concentration of the air quality particles (PM2.5 and PM10). The proposed deep learning model can automatically learn the intrinsic correlation among the pollutants in different location. Also, the meteorological and the pollution gas information at each location are fully utilized, which is beneficial for the performance of the model. We used multiple one-dimension convolutional neural network (CNN) layers to extract the local spatial features and a stacked Bi-LSTM layer to learn the spatiotemporal correlation of air quality particles. In addition, we used a stacked deep autoencoder to encode the essential transformation patterns of the pollution gas and the meteorological data, since they are very important for providing useful information that can significantly improve the prediction of the air quality particles. Finally, in order to reduce the training time and the resource consumption, we used a distributed deep leaning approach called data parallelism, which has never been used to tackle the problem of air quality forecasting. We evaluated our approach with extensive experiments based on the data collected in Busan metropolitan city. The results reveal the superiority of our framework over ten baseline models and display how the distributed deep learning model can significantly improve the training time and even the prediction accuracy.

Indoor and outdoor air quality monitoring in hemp house — Case study

2011 ◽  
Vol 6 (3) ◽  
pp. 63-72 ◽  
Author(s):  
Jarmila Rimbalová ◽  
Silvia Vilčeková ◽  
Adriana Eštoková
Keyword(s):  
Air Quality ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Outdoor Air ◽  
Outdoor Air Quality ◽  
Indoor And Outdoor
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