- Tytuł:
- PM 2.5 modelling during paddy stubble burning months using artificial intelligence techniques
- Autorzy:
-
Sangwan, V.
Deswal, S. - Powiązania:
- https://bibliotekanauki.pl/articles/2055747.pdf
- Data publikacji:
- 2022
- Wydawca:
- Stowarzyszenie Komputerowej Nauki o Materiałach i Inżynierii Powierzchni w Gliwicach
- Tematy:
-
PM2.5
ANN
random forest
SVM
pollution
burning
stubble
sztuczne sieci neuronowe
lasy losowe
maszyna wektorów nośnych
zanieczyszczenia
spalanie
ścierń - Opis:
- Purpose: In this study, the artificial intelligence techniques namely Artificial Neural Network, Random Forest, and Support Vector Machine are employed for PM 2.5 modelling. The study is carried out in Rohtak city of India during paddy stubble burning months i.e., October and November. The different models are compared to check their respective efficacies and also sensitivity analysis is performed to know about the most vital parameter in PM 2.5 modelling. Design/methodology/approach: The air pollution data of October and November months from the year 2016 to 2020 was collected for the study. The months of October and November are chosen as paddy stubble burning and major festivities using fireworks occur during these months. The untoward data entries viz. zero values, blank data, etc. were eliminated from the gathered data set and thereafter 231 observations of each parameter were left for the conduct of the presented study. The different models i.e., ANN, RF, SVM, etc. had PM 2.5 as an output variable while relative humidity, sulfur dioxide, nitrogen dioxide, nitric oxide, carbon monoxide, ozone, temperature, solar radiation, wind direction and wind speed acted as input variables. The prototypes created from the training data set are verified on the testing data set. A sensitivity analysis is also done to quantify impact of various parameters on output variable i.e., PM 2.5. Findings: The performance of the SVM_RBF based model turned out to be the best with the performance parameters being the coefficient of determination, root mean square error, and mean absolute error. In the sensitivity test, sulphur dioxide (SO2) was adjudged as the most vital variable. Research limitations/implications: The quantification capacity of the generated models may go beyond the used data set of observations. Practical implications: The artificial intelligence techniques provide precise estimation and forecasting of PM 2.5 in the air during paddy stubble burning months of October and November. Originality/value: Unlike the past research work that focus on modelling of various air pollution parameters, this study in specific focuses on the modelling of most vital air pollutant i.e., PM 2.5 that too specifically during the paddy stubble burning months of October and November when the air pollution is at its peak in northern India.
- Źródło:
-
Journal of Achievements in Materials and Manufacturing Engineering; 2022, 110, 1; 16--26
1734-8412 - Pojawia się w:
- Journal of Achievements in Materials and Manufacturing Engineering
- Dostawca treści:
- Biblioteka Nauki
Artykuł