Due to increase in industrial and anthropogenic activities, air pollution has been a serious environmental problem all over the world. It was found that harmful emission into the air is a symbol for environmental force that affects seriously man’s health, natural life and agriculture; thus leading to major loss of the nation’s economy. In this paper, the prediction of the surface ozone layer problem is explored. A comparison between two types of Artificial Neural Networks (ANN) (i.e. back propagation and Radial Basis Functions (RBF) networks) and the Support Vector Machines (SVM) techniques for short prediction of surface ozone is conclusively demonstrated. Three models which predict the expected values of the surface ozone based on three variables (i.e. Nitrogen-di-oxide, temperature and Relative Humidity) will be presented.
목차
Abstract 1. Introduction 2. Area of Study and Data Description 3. Artificial Neural Network 3.1. FF-ANN 3.2. RBF-ANN 4. Support Vector Machines 5. Model Evaluation 6. Experimental Results 7. Conclusion and Future Work References
키워드
Air pollutionSurface ozoneback propagation neural networkRadial Basis Function (RBF) neural networkSupport Vector Machines
보안공학연구지원센터(IJAST) [Science & Engineering Research Support Center, Republic of Korea(IJAST)]
설립연도
2006
분야
공학>컴퓨터학
소개
1. 보안공학에 대한 각종 조사 및 연구
2. 보안공학에 대한 응용기술 연구 및 발표
3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최
4. 보안공학 기술의 상호 협조 및 정보교환
5. 보안공학에 관한 표준화 사업 및 규격의 제정
6. 보안공학에 관한 산학연 협동의 증진
7. 국제적 학술 교류 및 기술 협력
8. 보안공학에 관한 논문지 발간
9. 기타 본 회 목적 달성에 필요한 사업
간행물
간행물명
International Journal of Advanced Science and Technology
간기
월간
pISSN
2005-4238
수록기간
2008~2016
십진분류
KDC 505DDC 605
이 권호 내 다른 논문 / International Journal of Advanced Science and Technology Vol.55