# Dispersion based Recurrent Neural Network Model for Methane Monitoring in Albertan Tailings Ponds

arXiv: https://arxiv.org/abs/2411.06741 · HTML: https://mlsanity.com/paper/2411.06741/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 68 references that are in the map.
- First posted on arXiv 11 Nov 2024.

## Builds on (most cited first, 6 of 6)

- [Extracting Sparse High-Dimensional Dynamics from Limited Data](https://mlsanity.com/paper/1707.08528/) (2017)
- [HARFE: Hard-Ridge Random Feature Expansion](https://mlsanity.com/paper/2202.02877/) (2022)
- [SPADE4: Sparsity and Delay Embedding based Forecasting of Epidemics](https://mlsanity.com/paper/2211.08277/) (2022)
- [Generalization Bound for Diffusion Models using Random Features](https://mlsanity.com/paper/2310.04417/) (2023)
- [Semiparametric forecasting and filtering: correcting low-dimensional model error in parametric models](https://mlsanity.com/paper/1502.07766/) (2015)
- [Deep Convolutional Neural Network for Plume Rise Measurements in Industrial Environments](https://mlsanity.com/paper/2302.07416/) (2023)

## Built on by (most cited first, 4 of 4)

- [Learning Coupled System Dynamics under Incomplete Physical Constraints and Missing Data](https://mlsanity.com/paper/2512.23761/) (2025)
- [Turning mechanistic models into forecasters by using machine learning](https://mlsanity.com/paper/2602.04114/) (2026)
- [CHAM-net: A Contrastive Hierarchical Adaptive Meta-network for Robust Global Methane Flux Prediction](https://mlsanity.com/paper/2606.00338/) (2026)
- [Learning PDE Time-Stepping with Neural Cellular Automata](https://mlsanity.com/paper/2608.30328/) (2026)

## Abstract (arXiv)

Bitumen extraction for the production of synthetic crude oil in Canada's Athabasca Oil Sands industry has recently come under spotlight for being a significant source of greenhouse gas emission. A major cause of concern is methane, a greenhouse gas produced by the anaerobic biodegradation of hydrocarbons in oil sands residues, or tailings, stored in settle basins commonly known as oil sands tailing ponds. In order to determine the methane emitting potential of these tailing ponds and have future methane projections, we use real-time weather data, mechanistic models developed from laboratory controlled experiments, and industrial reports to train a physics constrained machine learning model. Our trained model can successfully identify the directions of active ponds and estimate their emission levels, which are generally hard to obtain due to data sampling restrictions. We found that each active oil sands tailing pond could emit between 950 to 1500 tonnes of methane per year, whose environmental impact is equivalent to carbon dioxide emissions from at least 6000 gasoline powered vehicles. Although abandoned ponds are often presumed to have insignificant emissions, our findings indicate that these ponds could become active over time and potentially emit up to 1000 tonnes of methane each year. Taking an average over all datasets that was used in model training, we estimate that emissions around major oil sands regions would need to be reduced by approximately 12% over a year, to reduce the average methane concentrations to 2005 levels.