Summary of Predictive Modelling Of Air Quality Index (aqi) Across Diverse Cities and States Of India Using Machine Learning: Investigating the Influence Of Punjab’s Stubble Burning on Aqi Variability, by Kamaljeet Kaur Sidhu et al.
Predictive Modelling of Air Quality Index (AQI) Across Diverse Cities and States of India using Machine Learning: Investigating the Influence of Punjab’s Stubble Burning on AQI Variability
by Kamaljeet Kaur Sidhu, Habeeb Balogun, Kazeem Oluwakemi Oseni
First submitted to arxiv on: 11 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this research paper, the authors aim to develop a system for predicting Air Quality Index (AQI) based on air pollutant concentrations in the atmosphere. The study utilizes data from 22 monitoring stations across Delhi, Haryana, and Punjab, which is checked for null values and outliers. The researchers employ various machine learning models, including CatBoost, XGBoost, Random Forest, SVM regressor, time series model SARIMAX, and deep learning model LSTM, to predict AQI. Performance evaluation metrics such as MSE, RMSE, MAE, and R2 are used to compare the performance of different models, with Random Forest showing better results. This study contributes to addressing air pollution issues by providing a reliable method for predicting AQI. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Air pollution is a big problem that affects our health. To make things better, scientists want to create a system that can predict how bad the air quality is going to be. They used data from 22 stations in India to train their model. The model was tested with different machine learning techniques like Random Forest and deep learning. The results showed that one technique, called Random Forest, worked better than others. This study helps us understand how to make a system that can predict air quality so we can take steps to improve it. |
Keywords
» Artificial intelligence » Deep learning » Lstm » Machine learning » Mae » Mse » Random forest » Time series » Xgboost
