Prediction of PM2.5 Concentration Based on ARMA Model Based on Wavelet Transform

2020 ◽  
Author(s):  
Jingli Yang ◽  
Xiaofeng Zhou
Keyword(s):  
Wavelet Transform ◽  
Arma Model ◽  
Model Based ◽  
Pm2.5 Concentration

scholarly journals Application of a Hybrid Model Based on Echo State Network and Improved Particle Swarm Optimization in PM2.5 Concentration Forecasting: A Case Study of Beijing, China

2019 ◽  
Vol 11 (11) ◽  
pp. 3096 ◽  
Author(s):  
Xinghan Xu ◽  
Weijie Ren
Keyword(s):  
Air Pollution ◽  
Particle Swarm Optimization ◽  
Air Quality ◽  
Hybrid Model ◽  
Particle Swarm ◽  
Echo State Network ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Model Based ◽  
Pm2.5 Concentration

With the acceleration of urbanization, there is an increasing trend of heavy pollution. PM2.5, also known as fine particulate matter, refers to particles in the atmosphere with a diameter of less than or equal to 2.5 microns. PM2.5 has a serious impact on human life, a sustainable city, national economic development, and so on. How to forecast the PM2.5 concentration accurately, and then formulate a scientific air pollution prevention and monitoring program is of great significance. This paper proposes a hybrid model based on echo state network (ESN) and an improved particle swarm optimization (IPSO) algorithm for the Beijing air pollution problem, and provides a method for PM2.5 concentration forecasting. Firstly, the PSO algorithm is improved to speed up the search performance. Secondly, the optimal subset of the original data is selected by the convergence cross-mapping (CCM) method. Thirdly, the phase space reconstruction (PSR) process is combined with the forecasting model, and some parameters are optimized by the IPSO. Finally, the optimal variable subset is used to predict PM2.5 concentration. The 11-dimensional air quality data in Beijing from January 1 to December 31, 2016 are analyzed by the proposed method. The experimental results show that the hybrid method is superior to other comparative models in several evaluation indicators, both in one-step and multi-step forecasting of PM2.5 time series. The hybrid model has good application prospects in air quality forecasting and monitoring.

A transformation model based on dual-tree complex wavelet transform for non-rigid registration of 3D MRI images

2019 ◽  
Vol 17 (04) ◽  
pp. 1950025 ◽  
Author(s):  
Nima Torbati ◽  
Ahmad Ayatollahi
Keyword(s):  
Wavelet Transform ◽  
Magnetic Resonance Images ◽  
Transformation Model ◽  
Complex Wavelet Transform ◽  
Model Based ◽  
Current State ◽  
Gradient Descent Algorithm ◽  
Expansion Theory ◽  
Complex Wavelet ◽  
Spectral Transforms

Image registration is regarded as an important component of medical procedures. The present study aimed to introduce a new transformation model based on dual-tree complex wavelet transform (DT-CWT). To this aim, parametric registration methods was revised based on the function expansion theory and the gradient descent algorithm was used to introduce a general formulation for transformation models based on spatio-spectral transforms. Then, the performance of the proposed method was evaluated on a public dataset of 3D real magnetic resonance images (MRI) and compared with the transformation model based on wavelets. Finally, the performance of the proposed method was compared with the current state-of-the-art methods (IRTK, SyN and SPM-DARTEL). Based on the experimental results, the proposed method could deliver superior registration performance compared with the previous methods.

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