지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국지능시스템학회 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS Vol.19 No.4
- 발행연도
- 2019.12
- 수록면
- 242 - 249 (8page)
- DOI
- 10.5391/IJFIS.2019.19.4.242
이용수
초록· 키워드
오류제보하기
While we’ve made progress over the last century, qualities of air and drinking water are getting worse. For the health of the people, many countries recommend to wear a mask or not to go out in case of bad air quality. However, the lack of real-time measurement and poor prediction of the concentration of fine dust provide inaccurate alarm and information to residents. The real-time air quality measurement data is the big data of new field which should be complemented with the national meteorological data in order to meet the demands for the customers and public through the other relevant data. In this study we aim to build a system to predict PM (particulate matter)—microscopic dust generated by human activities, such as the burning of fossil fuels in vehicles, power plants and various industrial processes. We also develop an optimized path of sprinkler vehicles to improve air condition in a local city. The density of PM is affected by meteorological variables such as temperature, rainfall, humidity and wind speed. To predict the density of (fine) PM, we use machine learning techniques considering not only meteorological data from observatories, but spatial factors like pollutants, floating populations and traffic volumes of a certain location using LTE(Long Term Evolution) signals. We suggested a conceptual architecture of a system and illustrate a case study of preprocessing and analyzing the data gathering from a local province with results and discussions.
목차
Abstract
1. Introduction
2. Related Work
3. A Research Method
4. A Case Study
5. Building Models and Predicting the Density
6. Conclusion
References
참고문헌 (14)
참고문헌 신청
Matthew D. Adams / 2016 / Mapping real-time air pollution health risk for environmental management: Combining mobile and stationary air pollution monitoring with neural network models / Journal of Environmental Management 168:133~141
G. Grivas / 2006 / Artificial neural network models for prediction of PM10 hourly concentrations, in the Greater Area of Athens, Greece / Atmospheric Environment 40(7):1216~1229
Hong-di He / 2014 / Prediction of particulate matter at street level using artificial neural networks coupling with chaotic particle swarm optimization algorithm / Building and Environment 78:111~117
Xiang'zi Leng / 2017 / Prediction of size-fractionated airborne particle-bound metals using MLR, BP-ANN and SVM analyses / Chemosphere 180:513~522
J.B. Ordieres / 2005 / Neural network prediction model for fine particulate matter (PM2.5) on the US–Mexico border in El Paso (Texas) and Ciudad Juárez (Chihuahua) / Environmental Modelling & Software 20(5):547~559
G. Grivas / 2006 / Artificial neural network models for prediction of PM10 hourly concentrations, in the Greater Area of Athens, Greece / Atmospheric Environment 40(7):1216~1229
Hong-di He / 2014 / Prediction of particulate matter at street level using artificial neural networks coupling with chaotic particle swarm optimization algorithm / Building and Environment 78:111~117
Xiang'zi Leng / 2017 / Prediction of size-fractionated airborne particle-bound metals using MLR, BP-ANN and SVM analyses / Chemosphere 180:513~522
J.B. Ordieres / 2005 / Neural network prediction model for fine particulate matter (PM2.5) on the US–Mexico border in El Paso (Texas) and Ciudad Juárez (Chihuahua) / Environmental Modelling & Software 20(5):547~559
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