The global environment has become more polluted due to the rapid development of industrial technology. However, the existing machine learning prediction methods of air quality fail to analyze the reasons for the change of air pollution concentration because most of the prediction methods take more focus on the model selection. Since the framework of recent deep learning is very flexible, the model may be deep and complex in order to fit the dataset. Therefore, overfitting problems may exist in a single deep neural network model when the number of weights in the deep neural network model is large. Besides, the learning rate of stochastic gradient descent (SGD) treats all parameters equally, resulting in local optimal solution. In this paper, the Pearson correlation coefficient is used to analyze the inherent correlation of PM2.5 and other auxiliary data such as meteorological data, season data, and time stamp data which are applied to cluster for enhancing the performance. Extracted features are helpful to build a deep ensemble network (EN) model which combines the recurrent neural network (RNN), long short-term memory (LSTM) network, and gated recurrent unit (GRU) network to predict the PM2.5 concentration of the next hour. The weights of the submodel change with the accuracy of them in the validation set, so the ensemble has generalization ability. The adaptive moment estimation (Adam) an algorithm for stochastic optimization is used to optimize the weights instead of SGD. In order to compare the overall performance of different algorithms, the mean absolute error (MAE) and mean absolute percentage error (MAPE) are used as accuracy metrics in the experiments of this study. The experiment results show that the proposed method achieves an accuracy rate (i.e., MAE=6.19 and MAPE=16.20%) and outperforms the comparative models.

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The interpolation of fine-grained air quality has significant prospects in the area of air quality monitoring. The solution to this problem can effectively monitor the air quality of the areas by sparse air quality monitoring stations, so as to reduce the monitoring cost. Most of the existing researches are to solve the problem of air quality monitoring in the areas without stations by different interpolation methods. However, most of them are unable to verify the reliability of the proposed interpolation methods and can't provide a feasible range of interpolation methods at spatial resolutions. Therefore, this paper proposes an effectively unsupervised PM2.5 estimation method based on time distributed convolutional gated recurrent unit (TCGRU) and an interpolation method based on k-nearest neighbor inverse distance weighted (KIDW) to solve these problems. The model is trained by the data obtained by the interpolation method with missing one station in turn to get the monitoring capability of the areas without monitoring stations. Inverse distance weighted is combined with k-nearest neighbor to improve the performance of interpolation. In addition, time distributed convolutional neural network extracts the spatial features of air quality and storage time information for extracting temporal features by the gated recurrent units. A large number of experiments are carried out to evaluate the performance of the method by using the air quality dataset of Hubei Province, China. The experimental results show that the proposed model is effective for the monitoring of PM2.5 in the unsupervised areas and the performance (i.e. MAE=8.23, RMSE=11.27 and MAPE=19.14%) of estimating PM2.5 concentration is better than the comparative methods. INDEX TERMS Air quality modeling, spatio-temporal analysis, deep learning, inverse distance weighted, k-nearest neighbor.

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Recently, research studies on Location-Based Services (LBSs) based on networks including cellular network and Wi-Fi network have gradually become popular. Received Signal Strength Indicators (RSSIs) from the network can be detected and collected by mobile devices to estimate the locations without adopting the Global Positioning System (GPS). Previous research studies utilized the RSSIs of only cellular network or only Wi-Fi network to estimate location, which leads to a two-fold predicament involving error limits of cellular network-based methods and environmental constraints of Wi-Fi network-based methods. In addition, accommodating a highly temporal dependence of RSSI series data, this paper proposed a mobile positioning system based on Gated Recurrent Unit (GRU) with RSSIs from the heterogeneous network. GRU learns the temporal correlation of RSSIs and the relationship between RSSIs and GPS coordinates to estimate the locations of mobile devices. A large number of real experiments have been carried out to verify the performance of the proposed method, and experimental results demonstrate that the proposed method has lower errors (i.e., 5.86 m and 75% of errors within 4 m) compared with Neural Network (NN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM).

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