Evaluation of Machine Learning Algorithms for Air Quality Index (AQI) Prediction
Problem
Air quality in Uttarakhand, India has deteriorated with industrial and vehicle growth, so the study aimed to predict pollutant status and AQI for monitored cities.
Why it matters
Deteriorating air quality threatens public health in affected Indian regions.
What the AI did
Six machine learning algorithms were evaluated to classify AQI from pollutant data, and Random Forest was used to predict AQI values against actual values.
Results
Random Forest and Decision Tree achieved the highest accuracy of 99.0%.
Impact
High classification accuracy demonstrated on the studied dataset; no external validation or adoption stated.
Record
| Venue | Journal of Reliability and Statistical Studies |
|---|---|
| Published | 2023 |
| Work type | article |
| Authors | Alka Pant, Sanjay Sharma, Kamal Pant |
| Citations | 19 (OpenAlex, at import time) |
| References | 20 |
| Model or method | Random Forest, Decision Tree, SVM, KNN, Naive Bayes, Logistic Regression |
| Organization | not stated in source |
| Primary source | https://doi.org/10.13052/jrss0974-8024.1621 |
| Open access | diamond |
| Keywords | AQI prediction, machine learning, random forest, decision tree, India air quality |
Importance rating
real but narrower advance
Air quality deterioration from industry and automobiles threatens health in Indian regions, but the study covers monitored cities of one state with dataset-level results only; no external validation or adoption stated.
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