| Autor: |
Prabha, Gayathri Narayanan, Harshith, Akula Venkat, Rajesh, Uthara, Omanakuttan, Vishnu Kesav, Shiju, Amrita Varshini |
| Předmět: |
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| Zdroj: |
AIP Conference Proceedings; 2024, Vol. 3171 Issue 1, p1-12, 12p |
| Abstrakt: |
The pristine images of the skies are deceiving. The air living beings breathe lies invisible hazardous particles which can adversely harm them. One such compound is sulfur Dioxide. These gaseous compounds are born from the combustion of sulfur-containing fuels and various industrial processes. Accurate prediction of urban air quality is crucial for well-being. This research delves into a novel approach for forecasting SO2 concentrations in cities. The proposed method leverages readily available hourly data, over the span of a month, extracting informative trend attribute from past SO2. Through the application of Machine Learning Regression Models, this paper provides an innovative approach in predicting these SO2 values, Utilizing data from 5 diverse cities. We achieved our aim of identifying the best performing models, Decision Tree Regression and Random Forest regression, from all the models that were compared for this study according to the performance metrics. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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