Air Quality Prediction based on LSTM-Kalman Model

Song, Xijuan; Huang, Jijiang; Song, Dawei

doi:10.1109/itaic.2019.8785751

Cited by 35 publications

(14 citation statements)

References 9 publications

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“…In this work, we utilize the LSTM as our deep learning model. The LSTM has been demonstrated to be one of the top performers in terms of time-series prediction [80]- [105]. This is due to the capacity to retain more information over time through a combination of forget, input, and output gates embedded in the LSTM structure.…”

Section: Learning Modelmentioning

confidence: 99%

“…Regarding time-series prediction specifically, a variety of research has been conducted. For instance, Song et al [80] combined LSTM and Kalman Filter models for air quality prediction, achieving lower RMSE than the representative LSTM alone. Hajiaghayi and Vahedi [87] utilized LSTM models prediction and pattern extraction of code failure.…”

Section: Machine Learning On Sequential Datamentioning

confidence: 99%

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Towards Efficient and Intelligent Internet of Things Search Engine

et al. 2021

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The Internet of Things (IoT) has created a novel ecosystem for sensing and actuation throughout our world, enabling intelligently controlled autonomous systems to conserve energy, water crops, manage factories, and provide situation awareness on an unprecedented scale. As IoT progresses, the interest in IoT search engines, that is, search engines to find IoT devices and retrieve IoT data, has grown. While basic examples of IoT search engines exist, considerable challenges prevent the full realization of an efficient and intelligent IoT search engine that provides universal data service, scalable data communication and retrieval, and efficient querying of massively distributed heterogeneous devices and data. In this article, we first propose a generic framework for the IoT search engine, and then present a naming service for the IoT system, an essential component for an effective IoT search engine. We also outline some research challenges and possible solutions for building efficiency and intelligence in the IoT search engine. Further, we present a case study and seek to address a particular aspect of the query process for IoT search, namely efficient and timely query processing. Given the now obvious advances in machine learning, the potential for deep learning-based prediction to improve resource use, and thus query retrieval, is clear. In detail, we utilize Long-Short-Term Memory (LSTM) neural network architecture to predict aggregated query volumes to be preemptively applied and stored for immediate response. Combining several realistic IoT datasets, we explore the efficacy of simultaneously predicting multiple targets for predictive query retrieval.INDEX TERMS Machine learning, Internet of Things, search engine, edge intelligence, applications.

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Section: Learning Modelmentioning

confidence: 99%

Section: Machine Learning On Sequential Datamentioning

confidence: 99%

Towards Efficient and Intelligent Internet of Things Search Engine

et al. 2021

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“…Deeper LSTM networks may improve the forecasting performance but also result in higher computational complexity. Song et al (Song et al 2019) proposed a combined model of LSTM and Kalman filtering to predict concentration of several components that affect air quality. In (Bai et al 2019), a stacked auto-encoder model emphasizing on seasonality is proposed to forecast PM2.5 values in an hourly manner, throwing alarm messages whenever the forecasted value is above a threshold.…”

Section: Related Workmentioning

confidence: 99%

Air Quality Forecasting with Hybrid LSTM and Extended Stationary Wavelet Transform

Zeng

Jin

et al. 2021

Preprint

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Artificial intelligence (AI) technology-enhanced air quality forecasting is one of the most promising directions in the field of smart environment development. Despite recent advances in this area, two difficulties remain unsolved. First, multiple factors influence forecasting results, such as weather conditions, fuel usage and traffic conditions. These factors are usually unavailable in air quality sensor data. Second, traditional predicting models typically use the most recent training data, which neglects the historical data. In this study, we propose a hybrid deep learning model that embraces the merits of the stationary wavelet transform (SWT) and the nested long short term memory networks (NLSTM) to improve the prediction quality in the problem of hour-ahead air quality forecasting. The proposed method decomposes the original PM2.5 data into several more stationary sub-signals with different resolutions using an extended SWT algorithm. A framework that leverages several NLSTM recurrent neural networks is constructed to output forecasting results for different sub-signals, respectively. The final forecasting result is obtained by combining all sub-signal forecasting results using the inverse wavelet transform. Experiments on real-world data show that, accuracy-wise, our proposed method outperforms most of the existing prediction models in the literature. And the resulting forecasting curves of the proposed method are much closer to the real values without any lags, comparing with existing prediction models.

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“…However, the existing air quality prediction methods cannot effectively capture the complex nonlinearity of air quality, like PM2.5 concentrations. The prediction models based on deep learning [9][10][11][12][13][14] can extract the features existing in the air quality data and can achieve higher prediction accuracy. Some methods [15][16][17][18][19][20][21][22][23][24][25] simulate the temporal and spatial dependence of air quality data at the same time.…”

Section: Introductionmentioning

confidence: 99%

Air Quality Prediction Based on Integrated Dual LSTM Model

Chen

Guan

2021

IEEE Access

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Air quality prediction is an important reference for meteorological forecast and air controlling, but over fitting often occurs in prediction algorithms based on a single model. Aiming at the complexity of air quality prediction, a prediction method based on integrated dual LSTM (Long Short-Term Memory) model was proposed in this paper. Firstly, the Seq2Seq (Sequence to Sequence) technology is used to establish a single-factor prediction model which can obtain the predicted value of each component in air quality data, independently. Each component of air quality is regarded as time series data in the forecasting process. Then, the LSTM model with attention mechanism is used as the multi-factor prediction model. The influencing factors of air quality, like the data of neighboring stations and weather data, are considered in the model. Finally, XGBoosting (eXtreme Gradient Boosting) tree is used to integrate two models. The final prediction results can be obtained by accumulating the predicted values of the optimal subtree nodes. Through evaluation and analysis using five evaluation methods, the proposed method has better performance in terms of error and model expression power. Compared with other various models, the precision of prediction data has been greatly improved in our model.

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Air Quality Prediction based on LSTM-Kalman Model

Cited by 35 publications

References 9 publications

Towards Efficient and Intelligent Internet of Things Search Engine

Towards Efficient and Intelligent Internet of Things Search Engine

Air Quality Forecasting with Hybrid LSTM and Extended Stationary Wavelet Transform

Air Quality Prediction Based on Integrated Dual LSTM Model

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