CENTRAL ASIAN

Open access Sep 2026

Intelligent system for analysis and assessment of population health harm risks by air pollution level

An intelligent system that assigns an observation to one of six air quality index (AQI) classes, used as a proxy for the level of harm to population health, together with a comparative evaluation of the six models behind it is presented.

N. Rudnichenko, V. Vychuzhanin, D. Shvedov et al. · CEUR Workshop Proceedings, V... · 0 citations

Open access Aug 2026

A Conv-Attention Seasonal Aggregation Network (CASA-Net) for PM2.5 Concentration Forecasting Using US EPA Monitoring Data

The Convolutional Attention Seasonal Aggregation Network (CASA-Net) combining Conv1D feature extraction, Bidirectional Long Short-Term Memory (BiLSTM), Multi-Head Self-Attention (MHSA), residual learning, and sinusoidal Day-of-Year (DOY) seasonal embeddings is suggested.

Manasvi Garg · Impact in Computics · 0 citations

Open access Aug 2026

Temporal Feature Interaction Memory in Air Quality Multi Variate Time Series Prediction

A novel attention-based approach to predict Particle Matter concentrations using Recurrent Neural Networks using Recurrent Neural Networks (RNNs) to enhance predictive accuracy and achieves a dramatic reduction in RMSE, MAE and MAPE compared to baseline models.

M. Bavani, M. Augasta · International journal of com... · 0 citations

Conference Aug 2026

Enhancing Air Quality Prediction through CNN-LSTM Based Deep learning Model:

Air pollution has transformed into a critical environmental issue which endangers human health in nations that undergo rapid industrial development such as India. Air quality prediction requires precise outcomes because its two main purposes include creating early warning systems and designing successful public policie...

Davinder Kumar, Inderdeep Kaur, Anupam Mittal · International Conference on... · 0 citations

Sep 2026

Deep neural network-based prediction of air quality index (AQI) for municipal corporation station, Solapur, Maharashtra State, India

The efficiency of the proposed DNN model is proved, and the comparative analysis with the baseline models, such as Linear Regression and Support Vector Regression, demonstrates that the proposed model is more effective than traditional techniques in identifying nonlinear dependencies in air pollution data.

M. Bankar, V. Patki, Sachin Pore et al. · Theoretical and Applied Clim... · 0 citations

Conference Open access 2026

Prediction of Shenzhen PM2.5 Hourly Concentration Based on LSTM

PM2.5 pollution remains a key public health issue in coastal megacities such as Shenzhen, where human activity patterns and meteorological conditions drive rapid short-term changes in air quality. Holiday periods can alter travel, industrial, and household emission patterns, creating unique challenges for accurate ai...

Ming-Hao Wang · ITM Web of Conferences · 0 citations