ABSTRACT:

Air pollution has emerged as one of the most critical environmental challenges, significantly impacting public health, climate, and urban sustainability. Monitoring and predicting air quality is vital for environmental planning and public health management. In this project, we propose a deep learning-based approach to predict the Air Quality Index (AQI) category using real-time environmental pollutant data. The system incorporates Long Short-Term Memory (LSTM) and Deep Neural Network (DNN) models to classify air quality into six categories as defined by standard AQI guidelines: Good, Satisfactory, Moderate, Poor, Very Poor, and Severe. We utilized a large dataset comprising atmospheric pollutant information collected from multiple air quality monitoring stations. The data underwent extensive preprocessing, including the removal of redundant attributes, handling of missing values, and feature normalization using Min-Max scaling. To address the class imbalance present in the AQI categories, Synthetic Minority Over-sampling Technique (SMOTE) was applied. The target variable, AQI_Bucket, was derived by mapping pollutant averages to categorical classes. Two deep learning models were designed and trained: a sequential LSTM model to leverage temporal correlations and a standard feedforward DNN model for comparison. The LSTM model was constructed with an input layer, LSTM units, and dense layers for classification, while the DNN model used multiple fully connected layers with ReLU and softmax activations. Both models were trained using sparse categorical cross-entropy loss and optimized using the Adam optimizer.
The system achieved impressive accuracy in classifying AQI buckets, with the LSTM model outperforming the DNN in most test cases. Furthermore, a user-friendly graphical interface was developed using Python's Tkinter library. The GUI allows users to input real-time pollutant values such as pollutant_min, pollutant_max, and pollutant_avg along with station-specific metadata. Upon submission, both models predict the air quality category, and the results are displayed along with an option to export the predictions. This project demonstrates the feasibility and effectiveness of deep learning techniques in environmental monitoring systems. The integration of LSTM networks with GUI-based user interaction provides a practical, real-time solution for predicting air quality levels. The system can be extended further with real-time sensor integration, cloud deployment, and ensemble learning methods to enhance robustness and accuracy.

1. Develop an Accurate Air Quality Prediction Model
The primary objective is to develop an accurate model to predict the Air Quality Index (AQI) using deep learning techniques, such as Long Short-Term Memory (LSTM) and Dense Neural Networks (DNN). These models will use historical pollutant data to forecast future AQI levels.

2. Implement Data Preprocessing and Feature Engineering
This objective focuses on cleaning the dataset, handling missing values, and encoding categorical features. The goal is to prepare the data for training by scaling the features and ensuring the dataset is balanced using techniques like SMOTE.

3. Compare Performance of LSTM and DNN Models
Another key objective is to compare the performance of LSTM and DNN models in predicting AQI. The comparison will be based on accuracy, F1 score, and other evaluation metrics to determine which model performs better for this task.

4. Optimize Model Architecture and Hyperparameters
To achieve the best performance, the models will undergo hyperparameter tuning and architectural adjustments. This includes experimenting with the number of layers, neurons, and batch sizes to optimize the models' ability to predict air quality effectively.

5. Analyze the Impact of Meteorological Features
This objective aims to assess how meteorological factors such as temperature, humidity, and wind speed influence AQI predictions. By incorporating these features into the models, we aim to improve prediction accuracy and understand their role in air quality dynamics.

6. Build a User-Friendly Prediction System
A practical goal of this project is to develop a user-friendly application that allows users to input real-time pollutant data and get AQI predictions. This system will be built with a graphical user interface (GUI) to make it accessible for non-technical users.