Journal
IEEE ACCESS
Volume 7, Issue -, Pages 69524-69534Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2019.2917277
Keywords
Air pollution; Internet of Things; forecasting; LSTM; GRU
Categories
Funding
- Major Program of the National Social Science Fund of China [17ZDA092]
- Electronic Information and Control of Fujian University Engineering Research Center Fund [EIC1704]
- National Key R&D Program of China [2018YFB1003205]
- National Natural Science Foundation of China [61173136, U1836208, U1536206, U1836110, 61602253, 61672294]
- Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) fund
- Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET) Fund, China
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With the continuous development of the Chinese economy and the gradual acceleration of urbanization, it has caused tremendous damage to the environment. The bad air environment seriously damages the physical and mental health of the people. The change in smog concentration will be affected by many realistic factors and exhibit nonlinear characteristics. The method proposed in this paper is to use the Internet of Things (IoT) technology to monitor the acquired data, process the data, and predict the next data using a neural network. The existing prediction models have limitations. They don't accurately capture the law between the concentration of haze and the factors affecting reality. It is difficult to accurately predict the nonlinear smog data. One algorithm proposed in this paper is a two-layer model prediction algorithm based on Long Short Term Memory Neural Network and Gated Recurrent Unit (LSTM&GRU). We set a double-layer Recurrent Neural Network to predict the PM2.5 value. This model is an improvement and enhancement of the existing prediction method Long Short Term Memory (LSTM). The experiment integrates data monitored by the IoT node and information released by the national environmental protection department. First, the data of 96 consecutive hours in four cities were selected as the experimental samples. The experimental results are close to the true value. Then, we selected daily smog data from 2014/1/1 to 2018/1/1 as a train and test dataset. It contains smog data for 74 city sites. The first 70% of the data was used for training and the rest for testing. The results of this experiment show that our model can play a better prediction.
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