Abstract
With the acceleration of industrialization and modernization, the problem of air pollution has become more and more prominent, which causing serious impact on people’s production and life. Therefore, it is of great practical significance and social value to realize the prediction of air quality index. This paper takes the Tianjin air quality data and meteorological data from 2017 to 2019 as an example. Firstly, random forest interpolation was used to fill in missing values in the data reasonably. Secondly, under the framework of deep learning in TensorFlow, Locally Linear Embedding (LLE) was used to choose multivariate data to reduce data dimensions and realize feature selection. Finally, a prediction model of the air quality index was established by using the Long Short-Term Memory (LSTM) neural network based on the data after dimension reduction. The experimental results show that the method has obvious effects in terms of dimensionality reduction and exponential prediction accuracy compared with Principal Component Analysis (PCA) and Back Propagation (BP).
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This paper is funded by the program of the key discipline “Applied Mathematics” of Shanghai Polytechnic University (XXKPY1604).
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Fang, H., Feng, Y., Zhang, L., Su, M., Yang, H. (2020). A Long Short-Term Memory Neural Network Model for Predicting Air Pollution Index Based on Popular Learning. In: Nah, Y., Kim, C., Kim, SY., Moon, YS., Whang, S.E. (eds) Database Systems for Advanced Applications. DASFAA 2020 International Workshops. DASFAA 2020. Lecture Notes in Computer Science(), vol 12115. Springer, Cham. https://doi.org/10.1007/978-3-030-59413-8_16
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