DOI: 10.1016/j.atmosenv.2014.11.050
Scopus记录号: 2-s2.0-84916608707
论文题名: Artificial intelligence based approach to forecast PM2.5 during haze episodes: A case study of Delhi, India
作者: Mishra D ; , Goyal P ; , Upadhyay A
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2015
卷: 102 起始页码: 239
结束页码: 248
语种: 英语
英文关键词: Artificial neural network
; Haze episode
; Multiple linear regression
; Neuro-fuzzy logic
; Statistical analysis
Scopus关键词: Fuzzy logic
; Linear regression
; Mean square error
; Neural networks
; Statistical methods
; Sulfur dioxide
; Wind
; Correlation coefficient
; Fine particulate matter
; Haze episode
; Meteorological parameters
; Multiple linear regressions
; Neuro-fuzzy logic
; Normalized mean square error
; Statistical measures
; Forecasting
; artificial neural network
; atmospheric pollution
; cancer
; concentration (composition)
; forecasting method
; haze
; numerical model
; oxide
; particulate matter
; relative humidity
; respiratory disease
; statistical analysis
; urban area
; air pollutant
; air pollution
; air quality control
; Article
; artificial intelligence
; artificial neural network
; comparative study
; correlation analysis
; forecasting
; fuzzy logic
; haze
; humidity
; India
; meteorology
; multiple linear regression analysis
; particulate matter
; predictive value
; pressure
; temperature
; urban area
; wind
; Delhi
; India
Scopus学科分类: Environmental Science: Water Science and Technology
; Earth and Planetary Sciences: Earth-Surface Processes
; Environmental Science: Environmental Chemistry
英文摘要: Delhi has been listed as the worst performer across the world with respect to the presence of alarmingly high level of haze episodes, exposing the residents here to a host of diseases including respiratory disease, chronic obstructive pulmonary disorder and lung cancer. This study aimed to analyze the haze episodes in a year and to develop the forecasting methodologies for it. The air pollutants, e.g., CO, O3, NO2, SO2, PM2.5 as well as meteorological parameters (pressure, temperature, wind speed, wind direction index, relative humidity, visibility, dew point temperature, etc.) have been used in the present study to analyze the haze episodes in Delhi urban area. The nature of these episodes, their possible causes, and their major features are discussed in terms of fine particulate matter (PM2.5) and relative humidity. The correlation matrix shows that temperature, pressure, wind speed, O3, and dew point temperature are the dominating variables for PM2.5 concentrations in Delhi. The hour-by-hour analysis of past data pattern at different monitoring stations suggest that the haze hours were occurred approximately 48% of the total observed hours in the year, 2012 over Delhi urban area. The haze hour forecasting models in terms of PM2.5 concentrations (more than 50μg/m3) and relative humidity (less than 90%) have been developed through artificial intelligence based Neuro-Fuzzy (NF) techniques and compared with the other modeling techniques e.g., multiple linear regression (MLR), and artificial neural network (ANN). The haze hour's data for nine months, i.e. from January to September have been chosen for training and remaining three months, i.e., October to December in the year 2012 are chosen for validation of the developed models. The forecasted results are compared with the observed values with different statistical measures, e.g., correlation coefficients (R), normalized mean square error (NMSE), fractional bias (FB) and index of agreement (IOA). The performed analysis has indicated that R has values 0.25 for MLR, 0.53 for ANN, and NF: 0.72, between the observed and predicted PM2.5 concentrations during haze hours invalidation period. The results show that the artificial intelligence implementations have a more reasonable agreement with the observed values. Finally, it can be concluded that the most convincing advantage of artificial intelligence based NF model is capable for better forecasting of haze episodes in Delhi urban area than ANN and MLR models. © 2014 Elsevier Ltd. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/82064
Appears in Collections: 气候变化事实与影响
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作者单位: Centre for Atmospheric Sciences, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, India
Recommended Citation:
Mishra D,, Goyal P,, Upadhyay A. Artificial intelligence based approach to forecast PM2.5 during haze episodes: A case study of Delhi, India[J]. Atmospheric Environment,2015-01-01,102