An IoT based Sensing System for Modeling and Forecasting Urban Air Quality

Barthwal, Anurag; Acharya, Debopam

doi:10.1007/s11277-020-07862-6

Cited by 16 publications

(16 citation statements)

References 29 publications

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“…However, these deterministic methods rely heavily on theoretical assumptions and may lack key knowledge of physical processes, which makes it difficult to explain the nonlinearity and heterogeneity of many influencing factors, leading to AQI prediction bias [24]. Time series models (such as autoregressive (AR) model, moving average (MA) model and autoregressive moving average (ARMA) model) are the first choice to deal with IAQI sequences [25,26]. Barthwal utilized ARMA and autoregressive integrated moving average (ARIMA) time series models to predict the daily average AQI of Delhi National Capital District, India, and achieved good performance [25].…”

Section: Related Workmentioning

confidence: 99%

“…Time series models (such as autoregressive (AR) model, moving average (MA) model and autoregressive moving average (ARMA) model) are the first choice to deal with IAQI sequences [25,26]. Barthwal utilized ARMA and autoregressive integrated moving average (ARIMA) time series models to predict the daily average AQI of Delhi National Capital District, India, and achieved good performance [25]. However, the diffusion evolution of air pollutants is a dynamic nonlinear process, and linear statistics and time series cannot reflect its complexity, and the prediction deviation is generally large.…”

Section: Related Workmentioning

confidence: 99%

“…Sequence-based models, such as RNN, LSTM, and GRU, have shown a considerable advantage in air pollutant prediction [25,34,35]. General sequence-based models take advantage of shared parameters and pass implicit states to extract the temporal dependency of the context.…”

Section: Deep Learning-based Modelsmentioning

confidence: 99%

“…Convolution-based models, such as causal convolution network (CCN) from the field of image processing have also been widely applied in time series prediction [24,25]. Convolution in the CCN architecture is causal, meaning that there is no information "leaking" into the past.…”

Section: Deep Learning-based Modelsmentioning

confidence: 99%

“…For the MA model, the order P = 3 of the model was determined first, and then the autocorrelation between historical data and current data was determined for prediction [43]. The ARMA model, based on non-stationary time series, was determined through stationarity test, model recognition and test, and the order p and q of the ARMA model were set to 5 and 6, respectively [25]. ANN could be regarded as a nonlinear mapping from the input space to the output space.…”

Section: Prediction Performance Analysis 431 Target Station Performan...mentioning

confidence: 99%

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Fine-Grained Individual Air Quality Index (IAQI) Prediction Based on Spatial-Temporal Causal Convolution Network: A Case Study of Shanghai

et al. 2022

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Accurate and fine-grained individual air quality index (IAQI) prediction is the basis of air quality index (AQI), which is of great significance for air quality control and human health. Traditional approaches, such as time series, recurrent neural network or graph convolutional network, cannot effectively integrate spatial-temporal and meteorological factors and manage the dynamic edge relationship among scattered monitoring stations. In this paper, a ST-CCN-IAQI model is proposed based on spatial-temporal causal convolution networks. Both the spatial effects of multi-source air pollutants and meteorological factors were considered via spatial attention mechanism. Time-dependent features in the causal convolution network were extracted by stacked dilated convolution and time attention. All the hyper-parameters in ST-CCN-IAQI were tuned by Bayesian optimization. Shanghai air monitoring station data were employed with a series of baselines (AR, MA, ARMA, ANN, SVR, GRU, LSTM and ST-GCN). Final results showed that: (1) For a single station, the RMSE and MAE values of ST-CCN-IAQI were 9.873 and 7.469, decreasing by 24.95% and 16.87% on average, respectively. R2 was 0.917, with an average 5.69% improvement; (2) For all nine stations, the mean RMSE and MAE of ST-CCN-IAQI were 9.849 and 7.527, respectively, and the R2 value was 0.906. (3) Shapley analysis showed PM10, humidity and NO2 were the most influencing factors in ST-CCN-IAQI. The Friedman test, under different resampling, further confirmed the advantage of ST-CCN-IAQI. The ST-CCN-IAQI provides a promising direction for fine-grained IAQI prediction.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Deep Learning-based Modelsmentioning

confidence: 99%

Section: Deep Learning-based Modelsmentioning

confidence: 99%

Section: Prediction Performance Analysis 431 Target Station Performan...mentioning

confidence: 99%

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Fine-Grained Individual Air Quality Index (IAQI) Prediction Based on Spatial-Temporal Causal Convolution Network: A Case Study of Shanghai

et al. 2022

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Adopting the Internet of Things Technology to Remotely Monitor COVID-19 Patients

Saidi

Kacem

Tounsi

et al. 2022

Lecture Notes in Computer Science

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The coronavirus known as COVID-19 is the topic of the hour all over the world. This virus has invaded the world with its invariants, which are characterized by their rapid spread. COVID-19 has impacted the health of people and the economy of countries. For that, laboratories, researchers, and doctors are in a race against time to find a cure for this pandemic. To combat this virus, cutting-edge technologies such as artificial intelligence, cloud computing, and big data have been put in place. In our work, we use Internet of Things (IoT) technology. The use of IoT in an efficient way can lead to detecting infected people and avoiding being contaminated. In this paper, we are interested in the remote medical monitoring of patients who have tested positive for COVID-19. We propose a meta-modeling technique to model the IoT architecture. Then we implement two IoT solutions that permit the remote medical monitoring of patients infected with COVID-19 and the respect of social distancing by instantiating correct models that conform to the proposed meta-model in order to mitigate the COVID-19 outbreak.

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