Predictive modelling of nitrogen dioxide using soft computing techniques in the Agra, Uttar Pradesh, India
Nitrogen dioxide (NO2) is one of the air pollutants which aggravates the human health as well as causes environmental issues. It is more causes respiratory problems due to acid rains. Agra is a major tourist destination spot in India also similarly air pollution also increased growing urbanization a...
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my.uniten.dspace-366732025-03-03T15:43:49Z Predictive modelling of nitrogen dioxide using soft computing techniques in the Agra, Uttar Pradesh, India Sihag P. Mehta T. Sammen S.S. Pande C.B. Puri D. Radwan N. 57195985799 58135855600 57192093108 57193547008 57190749797 56763877500 Agra India Uttar Pradesh Acid rain Air pollution Atmospheric pressure Carbon monoxide Data handling Errors Forestry Linear matrix inequalities Mean square error Nitrogen oxides Recurrent neural networks Sensitivity analysis Soft computing Wind Group method of data handling M5P Multivariate adaptive regression Nitrogen monoxide Nitrogen monoxide2 Random forests Random tree Reduced error pruning tree Reduced-error pruning Study areas atmospheric chemistry atmospheric pollution computer simulation multivariate analysis nitrogen dioxide regression analysis relative humidity Sulfur dioxide Nitrogen dioxide (NO2) is one of the air pollutants which aggravates the human health as well as causes environmental issues. It is more causes respiratory problems due to acid rains. Agra is a major tourist destination spot in India also similarly air pollution also increased growing urbanization and traffic reflux. The current study aims to predicted the NO2 episodes in the Agra city using soft computing models namely, M5P, Random Forest (RF), Group method of data handling (GMDH), Multivariate adaptive regression (MARS), Reduced error pruning tree (REP Tree) and Random tree (RT). The models were generated using 1116 observations, from 2015 to 2020 with input parameters such as Particulate matter (PM2.5), Nitrogen monoxide (NO), Oxides of nitrogen (NOX), Sulphur dioxide (SO2), Carbon monoxide (CO), Ozone (O), Benzene (Be), Toluene, Relative humidity (RH), Wind speed (WS), Wind direction (WD), Solar radiation (SR), Barometric pressure (BP) and Xylene. The performance of each model was evaluated based on the six statistical indices, namely correlation coefficient (CC), root mean square error (RMSE), mean absolute error (MAE), normalized root of mean squared relative error (NRMSE), Willmott's Index (WI), and Legates and McCabe's Index (LMI). The performance evaluation models results of study area, Box plot and Taylor's diagram indicated that M5P is the outperforming model among others with testing CC = 0.9543, RMSE = 5.8006, MAE = 3,9204, NRMSE = 0.1512, WI = 0.9744, and LMI = 0.7549. Based on the sensitivity analysis indicated that NOx is the most influential parameter followed by WD and CO. These results of study area can be helpful to understanding the air pollution causes, health issues, and future NO2 levels around the study area with useful results for air pollution monitoring policy and development. ? 2024 Elsevier Ltd Final 2025-03-03T07:43:49Z 2025-03-03T07:43:49Z 2024 Article 10.1016/j.pce.2024.103589 2-s2.0-85189745092 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189745092&doi=10.1016%2fj.pce.2024.103589&partnerID=40&md5=f99d043dff4a9af5229ca40f5f2c4394 https://irepository.uniten.edu.my/handle/123456789/36673 134 103589 Elsevier Ltd Scopus |
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Agra India Uttar Pradesh Acid rain Air pollution Atmospheric pressure Carbon monoxide Data handling Errors Forestry Linear matrix inequalities Mean square error Nitrogen oxides Recurrent neural networks Sensitivity analysis Soft computing Wind Group method of data handling M5P Multivariate adaptive regression Nitrogen monoxide Nitrogen monoxide2 Random forests Random tree Reduced error pruning tree Reduced-error pruning Study areas atmospheric chemistry atmospheric pollution computer simulation multivariate analysis nitrogen dioxide regression analysis relative humidity Sulfur dioxide |
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Agra India Uttar Pradesh Acid rain Air pollution Atmospheric pressure Carbon monoxide Data handling Errors Forestry Linear matrix inequalities Mean square error Nitrogen oxides Recurrent neural networks Sensitivity analysis Soft computing Wind Group method of data handling M5P Multivariate adaptive regression Nitrogen monoxide Nitrogen monoxide2 Random forests Random tree Reduced error pruning tree Reduced-error pruning Study areas atmospheric chemistry atmospheric pollution computer simulation multivariate analysis nitrogen dioxide regression analysis relative humidity Sulfur dioxide Sihag P. Mehta T. Sammen S.S. Pande C.B. Puri D. Radwan N. Predictive modelling of nitrogen dioxide using soft computing techniques in the Agra, Uttar Pradesh, India |
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Nitrogen dioxide (NO2) is one of the air pollutants which aggravates the human health as well as causes environmental issues. It is more causes respiratory problems due to acid rains. Agra is a major tourist destination spot in India also similarly air pollution also increased growing urbanization and traffic reflux. The current study aims to predicted the NO2 episodes in the Agra city using soft computing models namely, M5P, Random Forest (RF), Group method of data handling (GMDH), Multivariate adaptive regression (MARS), Reduced error pruning tree (REP Tree) and Random tree (RT). The models were generated using 1116 observations, from 2015 to 2020 with input parameters such as Particulate matter (PM2.5), Nitrogen monoxide (NO), Oxides of nitrogen (NOX), Sulphur dioxide (SO2), Carbon monoxide (CO), Ozone (O), Benzene (Be), Toluene, Relative humidity (RH), Wind speed (WS), Wind direction (WD), Solar radiation (SR), Barometric pressure (BP) and Xylene. The performance of each model was evaluated based on the six statistical indices, namely correlation coefficient (CC), root mean square error (RMSE), mean absolute error (MAE), normalized root of mean squared relative error (NRMSE), Willmott's Index (WI), and Legates and McCabe's Index (LMI). The performance evaluation models results of study area, Box plot and Taylor's diagram indicated that M5P is the outperforming model among others with testing CC = 0.9543, RMSE = 5.8006, MAE = 3,9204, NRMSE = 0.1512, WI = 0.9744, and LMI = 0.7549. Based on the sensitivity analysis indicated that NOx is the most influential parameter followed by WD and CO. These results of study area can be helpful to understanding the air pollution causes, health issues, and future NO2 levels around the study area with useful results for air pollution monitoring policy and development. ? 2024 Elsevier Ltd |
| author2 |
57195985799 |
| author_facet |
57195985799 Sihag P. Mehta T. Sammen S.S. Pande C.B. Puri D. Radwan N. |
| format |
Article |
| author |
Sihag P. Mehta T. Sammen S.S. Pande C.B. Puri D. Radwan N. |
| author_sort |
Sihag P. |
| title |
Predictive modelling of nitrogen dioxide using soft computing techniques in the Agra, Uttar Pradesh, India |
| title_short |
Predictive modelling of nitrogen dioxide using soft computing techniques in the Agra, Uttar Pradesh, India |
| title_full |
Predictive modelling of nitrogen dioxide using soft computing techniques in the Agra, Uttar Pradesh, India |
| title_fullStr |
Predictive modelling of nitrogen dioxide using soft computing techniques in the Agra, Uttar Pradesh, India |
| title_full_unstemmed |
Predictive modelling of nitrogen dioxide using soft computing techniques in the Agra, Uttar Pradesh, India |
| title_sort |
predictive modelling of nitrogen dioxide using soft computing techniques in the agra, uttar pradesh, india |
| publisher |
Elsevier Ltd |
| publishDate |
2025 |
| _version_ |
1825816242437488640 |
| score |
13.244413 |