[논문]Air Pollutants Tracing Model using Perceptron Neural Network and Non-negative Least Square

Yu, Suk-Hyun; Kwon, Hee-Yong

doi:10.9717/kmms.2013.16.12.1465

Air Pollutants Tracing Model using Perceptron Neural Network and Non-negative Least Square 원문보기

멀티미디어학회논문지 = Journal of Korea Multimedia Society, v.16 no.12, 2013년, pp.1465 - 1474

Yu, Suk-Hyun (Dept. of Information & Communications Engineering, Anyang Univ.) , Kwon, Hee-Yong (Dept. of Computer Science Engineering, Anyang Univ.)

Abstract ▼ AI-Helper

In this paper, air pollutant tracing models using perceptron neural network(PNN) and non-negative least square(NNLS) are proposed. When the measured values of the air pollution and the contribution concentration of each source by chemical transport modeling are given, they estimate and trace the amount of the air pollutants emission from each source. Two kinds of emissions data are used in the experiments : CH4 and N2O of Geumgo-dong landfill greenhouse gas, and PM10 of 17 areas in Northeast Asia and eight regions of the Korean Peninsula. Emission values were calculated using pseudo inverse method, PNN and NNLS. Pseudo inverse method could be used for the model, but it may have negative emission values. In order to deal with the problem, we used the PNN and NNLS methods. As a result, the estimation using the NNLS is closer to the measured values than that using PNN. The proposed tracing models have better utilization and generalization than those of conventional pseudo inverse model. It could be used more efficiently for air quality management and air pollution reduction.

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문제 정의

Then, we propose an air pollutant sources tracing model to estimate greenhouse gases, CH₄, N₂O and PM10. The purpose of research is the estimation of emissions of each source with greenhouse gas data in domestic and East Asia regions. When measured emissions and contribution concentrations of each source by a chemical transport modeling are given, it is an optimization problem finding emission values of each source to minimize the error between the measured values and the estimated ones.

제안 방법

In this paper, we propose air pollutant tracing models by minimizing the error between the measured values and the estimated ones, when measured emissions and contribution concentrations of each source by a chemical transport modeling are given. The study is conducted with pseudo inverse method, PNN, and NNLS to estimate the emissions.
In this paper, we propose air pollutant tracing models by minimizing the error between the measured values and the estimated ones, when measured emissions and contribution concentrations of each source by a chemical transport modeling are given. The study is conducted with pseudo inverse method, PNN, and NNLS to estimate the emissions. Pseudo inverse method estimates emissions closest to the measured ones, but there exist negative emissions.
In this paper, we have collected the INTEX-B emission inventory data of greenhouse gases in Geumgo-dong landfill and East Asia region. Then, we propose an air pollutant sources tracing model to estimate greenhouse gases, CH₄, N₂O and PM10. The purpose of research is the estimation of emissions of each source with greenhouse gas data in domestic and East Asia regions.

대상 데이터

5 data sets are collected. The emission gases to be estimated are CH₄ and N₂O.

이론/모형

In this paper, three methods were studied. First, emissions of each source was estimated using the pseudo inverse method. There was a small error between the measured emissions and the estimated ones, but the negative estimate of the emissions occurred, so it should be improved.
Then we resolve the pseudo inverse method's defect of negative emission problem. Third, in order to reduce the error between measured emission values and the ones calculated by the proposed PNN, the NNLS method was applied. The results calculated by the NNLS are closer to the measured emissions than those of the PNN.

참고문헌 (22)

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원문보기 상세보기
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원문보기 상세보기
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상세보기
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상세보기
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상세보기
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상세보기
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Air Pollutants Tracing Model using Perceptron Neural Network and Non-negative Least Square 원문보기

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Air Pollutants Tracing Model using Perceptron Neural Network and Non-negative Least Square 원문보기

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