Urban PM2.5 Concentration Prediction via Attention-Based CNN–LSTM

Urban particulate matter forecasting is regarded as an essential issue for early warning and control management of air pollution, especially fine particulate matter (PM2.5). However, existing methods for PM2.5 concentration prediction neglect the effects of featured states at different times in the past on future PM2.5 concentration, and most fail to effectively simulate the temporal and spatial dependencies of PM2.5 concentration at the same time. With this consideration, we propose a deep learning-based method, AC-LSTM, which comprises a one-dimensional convolutional neural network (CNN), long short-term memory (LSTM) network, and attention-based network, for urban PM2.5 concentration prediction. Instead of only using air pollutant concentrations, we also add meteorological data and the PM2.5 concentrations of adjacent air quality monitoring stations as the input to our AC-LSTM. Hence, the spatiotemporal correlation and interdependence of multivariate air quality-related time-series data are learned by the CNN–LSTM network in AC-LSTM. The attention mechanism is applied to capture the importance degrees of the effects of featured states at different times in the past on future PM2.5 concentration. The attention-based layer can automatically weigh the past feature states to improve prediction accuracy. In addition, we predict the PM2.5 concentrations over the next 24 h by using air quality data in Taiyuan city, China, and compare it with six baseline methods. To compare the overall performance of each method, the mean absolute error (MAE), root-mean-square error (RMSE), and coefficient of determination (R2) are applied to the experiments in this paper. The experimental results indicate that our method is capable of dealing with PM2.5 concentration prediction with the highest performance.

[1]  Jun Ma,et al.  Soft detection of 5-day BOD with sparse matrix in city harbor water using deep learning techniques. , 2019, Water research.

[2]  Kun Luo,et al.  Recurrent Neural Network and random forest for analysis and accurate forecast of atmospheric pollutants: A case study in Hangzhou, China , 2019, Journal of Cleaner Production.

[3]  Shi-Jinn Horng,et al.  Deep Air Quality Forecasting Using Hybrid Deep Learning Framework , 2018, IEEE Transactions on Knowledge and Data Engineering.

[4]  Rui Liu,et al.  Effective long short-term memory with differential evolution algorithm for electricity price prediction , 2018, Energy.

[5]  Ping-Huan Kuo,et al.  A Deep CNN-LSTM Model for Particulate Matter (PM2.5) Forecasting in Smart Cities , 2018, Sensors.

[6]  Min Li,et al.  Prediction of PM2.5 concentration based on the similarity in air quality monitoring network , 2018, Building and Environment.

[7]  Xiang Li,et al.  Long short-term memory neural network for air pollutant concentration predictions: Method development and evaluation. , 2017, Environmental pollution.

[8]  Jianjun He,et al.  Air pollution in China: Status and spatiotemporal variations. , 2017, Environmental pollution.

[9]  Jack Chin Pang Cheng,et al.  Estimation of the building energy use intensity in the urban scale by integrating GIS and big data technology , 2016 .

[10]  Zhiwei Ni,et al.  Multifractal detrended cross-correlation analysis between PM2.5 and meteorological factors , 2015 .

[11]  Alexander M. Rush,et al.  A Neural Attention Model for Abstractive Sentence Summarization , 2015, EMNLP.

[12]  Koji Zettsu,et al.  Dynamically pre-trained deep recurrent neural networks using environmental monitoring data for predicting PM2.5 , 2015, Neural Computing and Applications.

[13]  Yunpeng Wang,et al.  Long short-term memory neural network for traffic speed prediction using remote microwave sensor data , 2015 .

[14]  Yoshua Bengio,et al.  Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.

[15]  Ajith Kaduwela,et al.  Seasonal modeling of PM2.5 in California's San Joaquin Valley , 2014 .

[16]  Alex Graves,et al.  Recurrent Models of Visual Attention , 2014, NIPS.

[17]  Yu Zheng,et al.  U-Air: when urban air quality inference meets big data , 2013, KDD.

[18]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[19]  Ujjwal Kumar,et al.  A Wavelet-based Neural Network Model to Predict Ambient Air Pollutants’ Concentration , 2011 .

[20]  Ayse Betül Oktay,et al.  Forecasting air pollutant indicator levels with geographic models 3 days in advance using neural networks , 2010, Expert Syst. Appl..

[21]  Ari Karppinen,et al.  Meteorological dependence of size-fractionated number concentrations of urban aerosol particles , 2006 .

[22]  Harri Niska,et al.  Methods for imputation of missing values in air quality data sets , 2004 .

[23]  S. Gabel,et al.  Using Neural Networks , 2003 .

[24]  Philip Demokritou,et al.  Measurements of PM10 and PM2.5 particle concentrations in Athens, Greece , 2003 .

[25]  S. Hochreiter,et al.  Long Short-Term Memory , 1997, Neural Computation.

[26]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[27]  N. Jusoh,et al.  Evaluating Fuzzy Time Series and Artificial Neural Network for Air Pollution Index Forecasting , 2018 .

[28]  Chenyang Lu,et al.  Spatiotemporal distribution of indoor particulate matter concentration with a low-cost sensor network , 2018 .

[29]  K. Pearson VII. Note on regression and inheritance in the case of two parents , 1895, Proceedings of the Royal Society of London.