Using Neural Network to Improve Data Quality for Incinerator Risk Assessment
Date Issued
2006
Date
2006
Author(s)
Wang, Chao-Min
DOI
zh-TW
Abstract
When using the risk assessment method to examine the impact of dioxins released from municipal solid waste incinerators (MSWIs), we need correct data in all respects, especially the dioxins emission data provided by the operators of incinerators. Furthermore, the dioxin concentrations measured in MSWIs surroundings are usually quite different from those from the result of air dispersion model, such as ISCST. The difference makes it hard for decision makers to issue risk management strategies.
In order to address these issues, this study proposes methods of assessing the correctness of emission data and relating ambient concentration measurements to predictions from modeling. The methodologies of this research are developed based on the data mining theory. We adopt SOM to establish outlier analysis method of incinerator flue dioxins concentrations and suggest reasonable explanation to the unusual data. BPN is then used to simulate the ratio of the ISCST modeling value and the measured value, attempting to estimate observed ambient concentrations from the ISCST modeling results.
The result of study shows that there are 4 outlier data among the 33 incinerator flue dioxin measurement reports in SOM topology; we should avoid use of the 4 data in risk assessment.
In BPN neural network, there are 107 ambient air dioxin measurement reports from the 9 incinerators in Taiwan, and we use 90% data for training and 10% data for testing to simulate the ratio of ISCST-predicted values and the observed values. The MSE values are 0.0173 and 0.0150, respectively, meaning that the relation is not significant. Then we adopt data from 3 incinerators in the same area in Kaohsiung to build BPN neural network and get better result. Finally, we use SOM neural network to identify ambient air dioxins fingerprints and incinerator flue dioxins fingerprints. For the data collected at present, we find that the dioxins fingerprints in the ambient air are quite different from the dioxins fingerprints in the incinerator flues. In this situation, it is not appropriate to estimate the observed value via the ISCST modeling value and BPN neural network.
Subjects
戴奧辛
異常值檢測
自組織特徵映射網路
倒傳遞類神經網路
大氣擴散模式
dioxins
outlier mining
SOM
BPN
air dispersion model
Type
thesis
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