INLA-RF: A Hybrid Modeling Strategy for Spatio-Temporal Environmental Data
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915980257525760 |
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| author | Figueira, Mario Cameletti, Michela Patelli, Luca |
| author_facet | Figueira, Mario Cameletti, Michela Patelli, Luca |
| contents | Environmental processes often exhibit complex, non-linear patterns and discontinuities across space and time, posing significant challenges for traditional geostatistical modeling approaches. In this paper, we propose a hybrid spatio-temporal modeling framework that combines the interpretability and uncertainty quantification of Bayesian models -- estimated using the INLA-SPDE approach -- with the predictive power and flexibility of Random Forest (RF). Specifically, we introduce two novel algorithms, collectively named INLA-RF, which integrate a statistical spatio-temporal model with RF in an iterative two-stage framework. The first algorithm (INLA-RF1) incorporates RF predictions as an offset in the INLA-SPDE model, while the second (INLA-RF2) uses RF to directly correct selected latent field nodes. Both hybrid strategies enable uncertainty propagation between modeling stages, an aspect often overlooked in existing hybrid approaches. In addition, we propose a Kullback-Leibler divergence-based stopping criterion. We evaluate the predictive performance and uncertainty quantification capabilities of the proposed algorithms through two simulation studies. Results suggest that our hybrid approach enhances spatio-temporal prediction while maintaining interpretability and coherence in uncertainty estimates. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_18488 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | INLA-RF: A Hybrid Modeling Strategy for Spatio-Temporal Environmental Data Figueira, Mario Cameletti, Michela Patelli, Luca Methodology Environmental processes often exhibit complex, non-linear patterns and discontinuities across space and time, posing significant challenges for traditional geostatistical modeling approaches. In this paper, we propose a hybrid spatio-temporal modeling framework that combines the interpretability and uncertainty quantification of Bayesian models -- estimated using the INLA-SPDE approach -- with the predictive power and flexibility of Random Forest (RF). Specifically, we introduce two novel algorithms, collectively named INLA-RF, which integrate a statistical spatio-temporal model with RF in an iterative two-stage framework. The first algorithm (INLA-RF1) incorporates RF predictions as an offset in the INLA-SPDE model, while the second (INLA-RF2) uses RF to directly correct selected latent field nodes. Both hybrid strategies enable uncertainty propagation between modeling stages, an aspect often overlooked in existing hybrid approaches. In addition, we propose a Kullback-Leibler divergence-based stopping criterion. We evaluate the predictive performance and uncertainty quantification capabilities of the proposed algorithms through two simulation studies. Results suggest that our hybrid approach enhances spatio-temporal prediction while maintaining interpretability and coherence in uncertainty estimates. |
| title | INLA-RF: A Hybrid Modeling Strategy for Spatio-Temporal Environmental Data |
| topic | Methodology |
| url | https://arxiv.org/abs/2507.18488 |