Deep Hybrid Model Based on EMD with Classification by Frequency Characteristics for Long-Term Air Quality Prediction

Abstract

Air pollution (mainly PM2.5) is one of the main environmental problems about air quality. Air pollution prediction and early warning is a prerequisite for air pollution prevention and control. However, it is not easy to accurately predict the long-term trend because the collected PM2.5 data have complex nonlinearity with multiple components of different frequency characteristics. This study proposes a hybrid deep learning predictor, in which the PM2.5 data are decomposed into components by empirical mode decomposition (EMD) firstly, and a convolutional neural network (CNN) is built to classify all the components into a fixed number of groups based on the frequency characteristics. Then, a gated-recurrent-unit (GRU) network is trained for each group as the sub-predictor, and the results from the three GRUs are fused to obtain the prediction result. Experiments based on the PM2.5 data from Beijing verify the proposed model, and the prediction results show that the decomposition and classification can develop the accuracy of the proposed predictor for air pollution prediction greatly.

The modified extended Kalman filter based recursive estimation for Wiener nonlinear systems with process noise and measurement noise

“…The simulation results show that the proposed algorithms are effective. The proposed methods in this article can combine some mathematical strategies [63][64][65][66] and can be extended to study the filtering, estimation, and prediction problems of different engineering systems with colored noises [67][68][69][70][71][72] and can be applied to other literature studies [73][74][75][76] such as information processing and networked communication systems.…”

Deep Learning Predictor for Sustainable Precision Agriculture Based on Internet of Things System

“…For medium-term prediction, S t takes the other three components and we set n as 24. This means we used the data from the historical 24 h to predict the data of the future 24 h. The method proposed in this paper can be combined with other system identification methods [30][31][32] to study the modeling and prediction of other dynamic time series and random systems [33,34] and can be applied to other fields [35][36][37] and other signal modeling and control systems [6,[38][39][40].…”

Hybrid Deep Learning Predictor for Smart Agriculture Sensing Based on Empirical Mode Decomposition and Gated Recurrent Unit Group Model

“…The GRU is trained by the gradient descent algorithm and the parameters are continually updated until convergence. The methods proposed in this paper can be applied to other fields, such as water environment prediction and management control systems [75], IoT intelligent systems [76][77][78], and wireless sensor networks [79][80][81].…”