Literature DB >> 30029320

Research on air pollutant concentration prediction method based on self-adaptive neuro-fuzzy weighted extreme learning machine.

Yongan Li1, Peng Jiang2, Qingshan She3, Guang Lin4.   

Abstract

In order to improve the prediction accuracy and real-time of the air pollutant concentration prediction, this paper proposes self-adaptive neuro-fuzzy weighted extreme learning machine (ANFIS-WELM) based on the weighted extreme learning machine (WELM) and the adaptive neuro-fuzzy inference system (ANFIS) combined air pollutant concentration prediction method. Firstly, Gaussian membership function parameters are selected to fuzzify the input values and calculate the membership degree of each input variable. Secondly, corresponding fuzzy rules are activated, and the firing strength is normalized to calculate the output matrix of hidden nodes. Then, the optimal parameters (C, M), weights are assigned to weighted ELM by using locally weighted linear regression, and the regularized WELM target formula with equality constraint is optimized by the Karush-Kuhn-Tucker (KKT) conditions, the output weight matrix is calculated, and finally the prediction output matrix is calculated. Based on the air pollutant concentration data collected in Datong, Taiwan, the data on the pollutants containing carbon monoxide (CO), nitric oxide (NO), PM2.5 (particulate matter) and PM10, are selected by different historical time series lengths, using genetic algorithm-backpropagation neural network (GA-BPNN), support vector regression (SVR), extreme learning machine (ELM), WELM, ANFIS, regularized extreme learning adaptive neuro-fuzzy inference system (R-ELANFIS) and ANFIS-WELM are built for predict the concentration of each pollutant collected by single monitoring point in single-step time series. The experimental results show that the ANFIS-WELM presented in this paper has better prediction accuracy and real-time performance, realizes the prediction of multi-step time series on the basis of the ANFIS-WELM, and realizes the engineering application of the ANFIS-WELM algorithm package on the self-developed mobile source emissions online monitoring data center software system.
Copyright © 2018 Elsevier Ltd. All rights reserved.

Keywords:  Adaptive neuro-fuzzy inference system; Air pollutant concentration predictions; Locally weighted linear regression; Weighted extreme learning machine

Mesh:

Substances:

Year:  2018        PMID: 30029320     DOI: 10.1016/j.envpol.2018.05.072

Source DB:  PubMed          Journal:  Environ Pollut        ISSN: 0269-7491            Impact factor:   8.071


  4 in total

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Authors:  Chunyi Zhang
Journal:  Comput Intell Neurosci       Date:  2022-05-31

3.  ADFIST: Adaptive Dynamic Fuzzy Inference System Tree Driven by Optimized Knowledge Base for Indoor Air Quality Assessment.

Authors:  Jagriti Saini; Maitreyee Dutta; Gonçalo Marques
Journal:  Sensors (Basel)       Date:  2022-01-28       Impact factor: 3.576

4.  A Novel Air Quality Early-Warning System Based on Artificial Intelligence.

Authors:  Xinyue Mo; Lei Zhang; Huan Li; Zongxi Qu
Journal:  Int J Environ Res Public Health       Date:  2019-09-20       Impact factor: 3.390

  4 in total

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