A Self-organizing LSTM-Based Approach to PM2.5 Forecast

Liu, Xiaodong; Liu, Qi; Zou, Yanyun; Wang, Guizhi

Abstract

Nanjing has been listed as the one of the worst performers across China with respect to the high level of haze-fog, which impacts people's health greatly. For the severe condition of haze-fog, PM2.5 is the main cause element of haze-fog pollution in China. So it’s necessary to forecast PM2.5 concentration accurately. In this paper, an artificial intelligence method is employed to fore-cast PM2.5 in Nanjing. At the data pre-processing stage, the main factors among the air pollutants (O3, NO2, SO2, CO, etc.) as well as meteorological parameters (pressure, wind direction, temperature, etc.) that affect PM2.5 are selected, and these factors of previous hours are as input data to predict PM2.5 concentration of next hours. Considering the air pollutants and meteorological data are typical time series data, a special recurrent neural network, which is called long short term memory (LSTM) network, is applied in this paper. To determine the amount of nodes in the hidden layer, a self-organizing method is used to automatically adjust the hidden nodes during the training phase. Finally, the PM2.5 concentrations of the next 1 hour, 4 hours, 8 hours, and 12 hours are predicted separately by using the self-organizing LSTM network based approach. The experimental result has been validated and compared to other algorithms, which reflects the proposed method performs best.

Citation

Liu, X., Liu, Q., Zou, Y., & Wang, G. (2018, June). A Self-organizing LSTM-Based Approach to PM2.5 Forecast. Presented at 4th International Conference on Cloud Computing and Security (ICCCS 2018), Haikou, China

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 4th International Conference on Cloud Computing and Security (ICCCS 2018)
• Start Date: Jun 8, 2018
• End Date: Jun 10, 2018
• Acceptance Date: Apr 20, 2018
• Online Publication Date: Sep 13, 2018
• Publication Date: Sep 13, 2018
• Deposit Date: Jun 22, 2018
• Publicly Available Date: Jun 22, 2018
• Publisher: Springer
• Pages: 683-693
• Series Title: Lecture Notes in Computer Science
• Series Number: 11066
• Series ISSN: 0302-9743
• Book Title: Proceedings of the 4th International Conference on Cloud Computing and Security (ICCCS 2018)
• ISBN: 978-3-030-00014-1
• DOI: https://doi.org/10.1007/978-3-030-00015-8_59
• Keywords: Haze-fog, PM2.5 Forecasting, Selecting Main Factors, Time Series Data, LSTM network, Self-organizing Algorithm
• Public URL: http://researchrepository.napier.ac.uk/Output/1233511
• Contract Date: Jun 21, 2018