# Learning Non-Stationary Space-Time Models for Environmental Monitoring

arXiv: https://arxiv.org/abs/1804.10535 · HTML: https://mlsanity.com/paper/1804.10535/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Apr 2018.

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

- [Non-stationary and Sparsely-correlated Multi-output Gaussian Process with Spike-and-Slab Prior](https://mlsanity.com/paper/2409.03149/) (2024)
- [STACI: Spatio-Temporal Aleatoric Conformal Inference](https://mlsanity.com/paper/2505.21658/) (2025)
- [Remaining useful life prediction with uncertainty quantification: development of a highly accurate model for rotating machinery](https://mlsanity.com/paper/2109.11579/) (2021)
- [Towards High Resolution Probabilistic Coastal Inundation Forecasting from Sparse Observations](https://mlsanity.com/paper/2505.05381/) (2025)

## Abstract (arXiv)

One of the primary aspects of sustainable development involves accurate understanding and modeling of environmental phenomena. Many of these phenomena exhibit variations in both space and time and it is imperative to develop a deeper understanding of techniques that can model space-time dynamics accurately. In this paper we propose NOSTILL-GP - NOn-stationary Space TIme variable Latent Length scale GP, a generic non-stationary, spatio-temporal Gaussian Process (GP) model. We present several strategies, for efficient training of our model, necessary for real-world applicability. Extensive empirical validation is performed using three real-world environmental monitoring datasets, with diverse dynamics across space and time. Results from the experiments clearly demonstrate general applicability and effectiveness of our approach for applications in environmental monitoring.