# Using Deep Learning to Extend the Range of Air-Pollution Monitoring and Forecasting

arXiv: https://arxiv.org/abs/1810.09425 · HTML: https://mlsanity.com/paper/1810.09425/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2018.

## Built on by (most cited first, 6 of 6)

- [A spatio-temporal LSTM model to forecast across multiple temporal and spatial scales](https://mlsanity.com/paper/2108.11875/) (2021)
- [Physics-Informed Deep Learning to Reduce the Bias in Joint Prediction of Nitrogen Oxides](https://mlsanity.com/paper/2308.07441/) (2023)
- [Forecasting Soil Moisture Using Domain Inspired Temporal Graph Convolution Neural Networks To Guide Sustainable Crop Management](https://mlsanity.com/paper/2212.06565/) (2022)
- [From Limited Annotated Raw Material Data to Quality Production Data: A Case Study in the Milk Industry (Technical Report)](https://mlsanity.com/paper/2204.12302/) (2022)
- [Development and Evaluation of Ensemble Learning-based Environmental Methane Detection and Intensity Prediction Models](https://mlsanity.com/paper/2312.10879/) (2023)
- [Machine Learning in Fish Farming](https://mlsanity.com/paper/2609.13919/) (2026)

## Abstract (arXiv)

Across numerous applications, forecasting relies on numerical solvers for partial differential equations (PDEs). Although the use of deep-learning techniques has been proposed, actual applications have been restricted by the fact the training data are obtained using traditional PDE solvers. Thereby, the uses of deep-learning techniques were limited to domains, where the PDE solver was applicable. We demonstrate a deep-learning framework for air-pollution monitoring and forecasting that provides the ability to train across different model domains, as well as a reduction in the run-time by two orders of magnitude. It presents a first-of-a-kind implementation that combines deep-learning and domain-decomposition techniques to allow model deployments extend beyond the domain(s) on which the it has been trained.