# Deep Air Quality Forecasting Using Hybrid Deep Learning Framework

arXiv: https://arxiv.org/abs/1812.04783 · HTML: https://mlsanity.com/paper/1812.04783/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2018.

## Built on by (most cited first, 30 of 33)

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- [Rethinking the Power of Timestamps for Robust Time Series Forecasting: A Global-Local Fusion Perspective](https://mlsanity.com/paper/2409.18696/) (2024)
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- [Hybrid Variational Autoencoder for Time Series Forecasting](https://mlsanity.com/paper/2303.07048/) (2023)
- [Machine Learning for Urban Air Quality Analytics: A Survey](https://mlsanity.com/paper/2310.09620/) (2023)
- [Time Series Forecasting (TSF) Using Various Deep Learning Models](https://mlsanity.com/paper/2204.11115/) (2022)
- [Deep-AIR: A Hybrid CNN-LSTM Framework for Air Quality Modeling in Metropolitan Cities](https://mlsanity.com/paper/2103.14587/) (2021)
- [HyMo: Vulnerability Detection in Smart Contracts using a Novel Multi-Modal Hybrid Model](https://mlsanity.com/paper/2304.13103/) (2023)
- [Spatiotemporal Predictions of Toxic Urban Plumes Using Deep Learning](https://mlsanity.com/paper/2406.02582/) (2024)
- [AirQualityBench: A Realistic Evaluation Benchmark for Global Air Quality Forecasting](https://mlsanity.com/paper/2605.05854/) (2026)
- [PlumeCityNet: Multi-Resolution Air Quality Forecasting](https://mlsanity.com/paper/2110.02661/) (2021)
- [AutoMC: Automated Model Compression based on Domain Knowledge and Progressive search strategy](https://mlsanity.com/paper/2201.09884/) (2022)
- [Dynamic Graph Neural Network with Adaptive Edge Attributes for Air Quality Predictions](https://mlsanity.com/paper/2302.09977/) (2023)
- [Novel Regression and Least Square Support Vector Machine Learning Technique for Air Pollution Forecasting](https://mlsanity.com/paper/2306.07301/) (2023)
- [Probabilistic Learning of Multivariate Time Series with Temporal Irregularity](https://mlsanity.com/paper/2306.09147/) (2023)
- [Phased Deep Spatio-temporal Learning for Highway Traffic Volume Prediction](https://mlsanity.com/paper/2308.06155/) (2023)
- [Multi-spatial Multi-temporal Air Quality Forecasting with Integrated Monitoring and Reanalysis Data](https://mlsanity.com/paper/2401.00521/) (2023)
- [ComS2T: A complementary spatiotemporal learning system for data-adaptive model evolution](https://mlsanity.com/paper/2403.01738/) (2024)
- [WaveCatBoost for Probabilistic Forecasting of Regional Air Quality Data](https://mlsanity.com/paper/2404.05482/) (2024)
- [Navigating the Smog: A Cooperative Multi-Agent RL for Accurate Air Pollution Mapping through Data Assimilation](https://mlsanity.com/paper/2407.12539/) (2024)
- [Can Deep Learning Trigger Alerts from Mobile-Captured Images?](https://mlsanity.com/paper/2501.03499/) (2025)
- [Air Quality Prediction with A Meteorology-Guided Modality-Decoupled Spatio-Temporal Network](https://mlsanity.com/paper/2504.10014/) (2025)
- [Out-of-Sample Hydrocarbon Production Forecasting: Time Series Machine Learning using Productivity Index-Driven Features and Inductive Conformal Prediction](https://mlsanity.com/paper/2508.14078/) (2025)
- [Real-Time Long Horizon Air Quality Forecasting via Group-Relative Policy Optimization](https://mlsanity.com/paper/2511.22169/) (2025)
- [A temporal deep learning framework for calibration of low-cost air quality sensors](https://mlsanity.com/paper/2604.21527/) (2026)
- [CombinationTS: A Modular Framework for Understanding Time-Series Forecasting Models](https://mlsanity.com/paper/2605.01231/) (2026)

## Abstract (arXiv)

Air quality forecasting has been regarded as the key problem of air pollution early warning and control management. In this paper, we propose a novel deep learning model for air quality (mainly PM2.5) forecasting, which learns the spatial-temporal correlation features and interdependence of multivariate air quality related time series data by hybrid deep learning architecture. Due to the nonlinear and dynamic characteristics of multivariate air quality time series data, the base modules of our model include one-dimensional Convolutional Neural Networks (1D-CNNs) and Bi-directional Long Short-term Memory networks (Bi-LSTM). The former is to extract the local trend features and spatial correlation features, and the latter is to learn spatial-temporal dependencies. Then we design a jointly hybrid deep learning framework based on one-dimensional CNNs and Bi-LSTM for shared representation features learning of multivariate air quality related time series data. We conduct extensive experimental evaluations using two real-world datasets, and the results show that our model is capable of dealing with PM2.5 air pollution forecasting with satisfied accuracy.