PURPOSES : This study analyzes the characteristics of generated fine particulate matter (PM2.5) and nitrogen oxide (NOX) at roadsides using a statistical method, namely, a generalized linear model (GLM). The study also investigates the applicability and capability of a machine learning methods such as a generalized regression neural network (GRNN) for predicting PM2.5 and NOX generations.
METHODS : To analyze the characteristics of PM2.5 and NOX generations at roadsides, data acquisition was conducted in a specific segment of roads, and PM2.5 and NOX prediction models were estimated using GLM. In addition, to investigate the applicability and capability of a machine learning methods, PM2.5 and NOX prediction models were estimated using a GRNN and were compared with models employing previously estimated GLMs using r-square, mean absolute deviation (MAD), mean absolute percentage error (MAPE), and root mean square error (RMSE) as parameters.
RESULTS : Results revealed that relative humidity, wind speed, and traffic volume were significant for both PM2.5 and NOX prediction models based on estimated models from a GLM. In addition, to compare the applicability and capability of the GLM and GRNN models (i.e., PM2.5 and NOX prediction models), the GRNN model of PM2.5 and NOX prediction was found to yield better statistical significance for r-square, MAD, MAPE, and RMSE as compared with the same parameters used in the GLM.
CONCLUSIONS : Analytical results indicated that a higher relative humidity and traffic volume could lead to higher PM2.5 and NOX concentrations. By contrast, lower wind speed could affect higher PM2.5 and NOX concentrations at roadsides. In addition, based on a comparison of two statistical methods (i.e., GLM and GRNN models used to estimate PM2.5 and NOX), GRNN model yielded better statistical significance as compared with GLM.

ABSTRACT
1. 서론
    1.1. 연구의 배경 및 목적
    1.2. 연구의 범위 및 내용
2. 이론적 배경
    2.1. 초미세먼지
    2.2. Generalized Regression Neural Network
3. 초미세먼지 및 질소산화물 예측모형 개발
    3.1. 데이터 수집 및 분석
    3.2. 초미세먼지 및 질소산화물 예측모형 개발
    3.3. 머신러닝을 통한 초미세먼지 및 질소산화물 예측모형 개발
4. 모형개발 결과 및 산정방법 비교분석
5. 결론
REFERENCES

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