Hybrid Deep Learning with Attention-Based Interpretability for PM2.5 Forecasting in Delhi Urban Environments.
TL;DR
The novelty of CORTA-Net lies in combining explicit CorrXGBoost-Rank feature screening, transfer-learning-based temporal encoding, MODIS fire-activity integration, and multi-head attention-based model-behavior analysis in a single reproducible PM2.5 forecasting pipeline.
Abstract
Short-term PM2.5 forecasting is challenging in Delhi because particulate matter concentrations are influenced by local emissions, meteorological variation, seasonal stagnation, and episodic fire-related pollution. This study presents CORTA-Net, a hybrid deep learning framework for PM2.5 forecasting using multi-source environmental data from 2012 to 2024. The input data includes hourly air-quality observations from CPCB (Central Pollution Control Board) /DPCC (Delhi Pollution Control Committee) monitoring stations, meteorological variables from the India Meteorological Department (IMD), and satellite-derived fire-count information from MODIS (Moderate Resolution Imaging Spectroradiometer) products. The proposed framework first applies CorrXGBoost-Rank feature selection to reduce redundant predictors and retain important pollutants, meteorological, temporal, and fire-related variables. The selected features are then arranged as supervised sliding-window sequences and processed using a transfer-learning-based LSTM encoder followed by a multi-head attention layer. The attention mechanism provides feature- and time-step-level interpretation of the PM2.5 forecast. CORTA-Net was evaluated using chronological training, testing, and validation partitions, as well as cross-validation. Compared with random forest, XGBoost, LSTM, and attention-LSTM baselines, the proposed framework reduced prediction error under the evaluated Delhi monitoring-station setting. The novelty of CORTA-Net lies in combining explicit CorrXGBoost-Rank feature screening, transfer-learning-based temporal encoding, MODIS fire-activity integration, and multi-head attention-based model-behavior analysis in a single reproducible PM2.5 forecasting pipeline. Practically, the framework can support short-term urban air-quality forecasting in data-rich monitoring environments where pollutant records, meteorological observations, and fire-activity indicators are available.