Using Machine Learning to Characterize and Predict Exposure Behavior- A Vulnerability Assessment Method for Soil and Groundwater Exposure
Date Issued
2016
Date
2016
Author(s)
Chia, Hao-Chun
Abstract
Health risk assessment is a process to estimate the adverse health effects in human who may be exposed to chemicals in contaminated environmental media. Health risk assessment also plays an important role in facing soil and groundwater contamination events. However, health risk assessment often takes a lot of time and money, especially in soil and groundwater cases, which is more difficult in pollution survey and remediation. In addition, variability is an inherent characteristic of a population; people vary substantially in their exposures and susceptibility to harmful effects of the exposures. Addressing variability is critical for health risk assessment, and it should be better characterized. A simplified assessment method is proposed in this study. To characterize the variability in exposures of a population. Decision tree, one of the most popular methods in machine learning, is used to extract information from 4188 historical questionnaires. Then we choose the most important factors that affect exposure behavior as exposure factors and susceptibility factors from the social and economic factors database, including acceptor basic information (age, sex, weight, occupation and acceptor type) .and socio-economic factors (income, rate of population served by tap water, education, population density, elders and farm rate). This study defines vulnerability as the combination of exposure factors and susceptibility factors, completes a vulnerability assessment, and produces exposure maps and vulnerability maps of 8 different exposure type in Taichung. 10 different exposure behaviors of soil and groundwater are characterized, the result shows that landuse is a important characteristic of soil and groundwater exposure; landuse of farm and occupation of farmer are both more likely to have high exposure; rate of population served by tap water is highly related to exposure type such as inhale exposure when taking a shower or bath and dermal exposure; education may help predict exposure to groundwater; the exposure characteristics of soil and groundwater are not the same and needs to be discuss separately; decision tree of whether exposure to groundwater has good prediction ability, the AUC is 0.9051. The study addressed one of the problem of conducting health risk assessment, which is related to resourse demand and difficulty to assess large area. Vulnerability assessment can be done quickly by socio-economics open data. Vulnerability assessment can be used as guidance for high pollution factories and industrial area management. In addition, when a place is determined to be more vulnerable, relevant policies should be put in place in order to protect the most vulnerable population and achieve environmental justice.
Subjects
Vulnerability
Variablity
Exposure Behavior
Data Mining
Machine Learning
Decision Tree
SDGs
Type
thesis
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