Indoor Air Quality Analysis Using Deep Learning with Sensor Data

Ahn, Jae-Seong; Shin, Dongil; Kim, Kyuho; Yang, Jihoon

doi:10.3390/s17112476

Cited by 75 publications

(33 citation statements)

References 11 publications

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“…The GRU unit is simpler and more efficient than the standard LSTM. It has become increasingly popular in natural language processing [23], speech recognition [30], traffic prediction [75] and air quality prediction [76].…”

Section: Gated Recurrent Unitmentioning

confidence: 99%

“…Today, CNN has been the dominate model in processing data with strong spatial correlation, e.g., images used in medical research [99], [100], [101] and public surveillance [13], [104], as well as geographic location data used in human mobility [42]. RNN has been the first choice for modelling sequence data with strong temporal dependencies, e.g., sensor time series of traffic flow or speed [40], [41], [75], air quality [10], [76], [80], human activity [43], [79], etc. This can be roughly used as a first guideline in selecting an appropriate model.…”

Section: Model Selection For Smart City Datamentioning

confidence: 99%

“…Input representation City data Reference RNN Time sequence data {x 1 , x 2 , ..., xt} traffic flow or speed (sensor) [40], [41], [75] air quality data (sensor) [10], [76], [80] healthcare data (sensor) [8] human activity (sensor) [43], [79] CNN 2D or 3D matrix  …”

Section: Modelmentioning

confidence: 99%

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A Survey on an Emerging Area: Deep Learning for Smart City Data

Chen

Wang

et al. 2019

IEEE Trans. Emerg. Top. Comput. Intell.

112

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Rapid urbanisation has brought about great challenges to our daily lives, such as traffic congestion, environmental pollution, energy consumption, public safety and so on. Research on smart cities aims to address these issues with various technologies developed for the Internet of Things. Very recently, the research focus has shifted towards processing of massive amount of data continuously generated within a city environment, e.g., physical and participatory sensing data on traffic flow, air quality, and healthcare. Techniques from computational intelligence have been applied to process and analyse such data, and to extract useful knowledge that helps citizens better understand their surroundings and informs city authorities to provide better and more efficient public services. Deep learning, as a relatively new paradigm in computational intelligence, has attracted substantial attention of the research community and demonstrated greater potential over traditional techniques. This paper provides a survey of the latest research on the convergence of deep learning and smart city from two perspectives: while the technique-oriented review pays attention to the popular and extended deep learning models, the application-oriented review emphasises the representative application domains in smart cities. Our study showed that there are still many challenges ahead for this emerging area owing to the complex nature of deep learning and wide coverage of smart city applications. We pointed out a number of future directions related to deep learning efficiency, emergent deep learning paradigms, knowledge fusion and privacy preservation, and hope these would move the relevant research one step further in creating truly distributed intelligence for smart cities.

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Section: Gated Recurrent Unitmentioning

confidence: 99%

Section: Model Selection For Smart City Datamentioning

confidence: 99%

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A Survey on an Emerging Area: Deep Learning for Smart City Data

Chen

Wang

et al. 2019

IEEE Trans. Emerg. Top. Comput. Intell.

112

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“…Li, Peng, Hu, Shao & Chi proposed an approach for predicting air quality based on spatiotemporal DL and essentially examined the correlation between the spatial and temporal data [14]. Ahn, Shin, Kim, & Yang took the advantage of sensor data to investigate changes in indoor air quality by adopting a DL method [15]. Li et al automatically dissected the air pollution process and developed a predictive platform for air quality that came from big-data analysis and recognition based on multidimensional historical pollution processes and the weather situation [16].…”

Section: State Of the Artmentioning

confidence: 99%

A Deep Belief Network Based Model for Urban Haze Prediction

Song

et al. 2018

Teh. vjesn.

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Abstract:In order to improve the accuracy of urban haze prediction, a novel deep belief network (DBN)-based model was proposed. Firstly, data pertaining to both air quality and the environment (e.g. meteorology) data was monitored and collected. The primary haze influencing elements were discovered by analyzing the correlations between each of the meteorological factors and haze. Secondly, a DBN combined with multilayer restricted Boltzmann machines and a single-layer back propagation network was applied. Thirdly, the meteorological data predictions were carried out by using a competitive adaptive-reweighed method. A stable model was established by big-data training and its accuracy was verified by experiments. Results demonstrate that the pollution haze occurs in accordance with regular laws, and is greatly affected by wind direction, atmospheric pressure, and seasons. The correlation coefficient (CC) between the actual haze value and the prediction of the proposed model is 0.8, and the mean absolute error (MAE) is 26 μg/m 3 . Compared with the traditional prediction algorithms, the CC is improved by 18 % on average, while the MAE is reduced by 15.7 μg/m 3 . The proposed method has a good prospect to predict haze and investigate the main causes of it. This study provides data support for urban haze prevention and governance.

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“…They are data-driven methods that do not require any a priori assumptions and thus have a wide range of applications. Commonly used artificial intelligence models include support vector regression [11], RBF neural networks [12], Elman neural networks [4], echo state networks [7], deep neural networks [13], extended Kalman filters [14], adaptive neuro-fuzzy inference systems (ANFISs) [15][16][17], etc. So as to improve the performance of single-model-based prediction models, a novel framework based on decomposition algorithm has been introduced for time series prediction [18].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Model Based on a Two-Layer Decomposition Approach and an Optimized Neural Network for Chaotic Time Series Prediction

Ren

2019

Symmetry

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The prediction of chaotic time series has been a popular research field in recent years. Due to the strong non-stationary and high complexity of the chaotic time series, it is difficult to directly analyze and predict depending on a single model, so the hybrid prediction model has become a promising and favorable alternative. In this paper, we put forward a novel hybrid model based on a two-layer decomposition approach and an optimized back propagation neural network (BPNN). The two-layer decomposition approach is proposed to obtain comprehensive information of the chaotic time series, which is composed of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD). The VMD algorithm is used for further decomposition of the high frequency subsequences obtained by CEEMDAN, after which the prediction performance is significantly improved. We then use the BPNN optimized by a firefly algorithm (FA) for prediction. The experimental results indicate that the two-layer decomposition approach is superior to other competing approaches in terms of four evaluation indexes in one-step and multi-step ahead predictions. The proposed hybrid model has a good prospect in the prediction of chaotic time series.

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Indoor Air Quality Analysis Using Deep Learning with Sensor Data

Cited by 75 publications

References 11 publications

A Survey on an Emerging Area: Deep Learning for Smart City Data

A Survey on an Emerging Area: Deep Learning for Smart City Data

A Deep Belief Network Based Model for Urban Haze Prediction

A Hybrid Model Based on a Two-Layer Decomposition Approach and an Optimized Neural Network for Chaotic Time Series Prediction

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