Knowledge distillation as a pathway toward next-generation intelligent ecohydrological modeling systems
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866914017195327488 |
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| author | Jiang, Long Yang, Yang Chui, Ting Fong May Thornwell, Morgan Gupta, Hoshin Vijai |
| author_facet | Jiang, Long Yang, Yang Chui, Ting Fong May Thornwell, Morgan Gupta, Hoshin Vijai |
| contents | Simulating ecohydrological processes is essential for understanding complex environmental systems and guiding sustainable management amid accelerating climate change and human pressures. Process-based models provide physical realism but can suffer from structural rigidity, high computational costs, and complex calibration, while machine learning (ML) methods are efficient and flexible yet often lack interpretability and transferability. We propose a unified three-phase framework that integrates process-based models with ML and progressively embeds them into artificial intelligence (AI) through knowledge distillation. Phase I, behavioral distillation, enhances process models via surrogate learning and model simplification to capture key dynamics at lower computational cost. Phase II, structural distillation, reformulates process equations as modular components within a graph neural network (GNN), enabling multiscale representation and seamless integration with ML models. Phase III, cognitive distillation, embeds expert reasoning and adaptive decision-making into intelligent modeling agents using the Eyes-Brain-Hands-Mouth architecture. Demonstrations for the Samish watershed highlight the framework's applicability to ecohydrological modeling, showing that it can reproduce process-based model outputs, improve predictive accuracy, and support scenario-based decision-making. The framework offers a scalable and transferable pathway toward next-generation intelligent ecohydrological modeling systems, with the potential extension to other process-based domains. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_01972 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Knowledge distillation as a pathway toward next-generation intelligent ecohydrological modeling systems Jiang, Long Yang, Yang Chui, Ting Fong May Thornwell, Morgan Gupta, Hoshin Vijai Machine Learning Geophysics Simulating ecohydrological processes is essential for understanding complex environmental systems and guiding sustainable management amid accelerating climate change and human pressures. Process-based models provide physical realism but can suffer from structural rigidity, high computational costs, and complex calibration, while machine learning (ML) methods are efficient and flexible yet often lack interpretability and transferability. We propose a unified three-phase framework that integrates process-based models with ML and progressively embeds them into artificial intelligence (AI) through knowledge distillation. Phase I, behavioral distillation, enhances process models via surrogate learning and model simplification to capture key dynamics at lower computational cost. Phase II, structural distillation, reformulates process equations as modular components within a graph neural network (GNN), enabling multiscale representation and seamless integration with ML models. Phase III, cognitive distillation, embeds expert reasoning and adaptive decision-making into intelligent modeling agents using the Eyes-Brain-Hands-Mouth architecture. Demonstrations for the Samish watershed highlight the framework's applicability to ecohydrological modeling, showing that it can reproduce process-based model outputs, improve predictive accuracy, and support scenario-based decision-making. The framework offers a scalable and transferable pathway toward next-generation intelligent ecohydrological modeling systems, with the potential extension to other process-based domains. |
| title | Knowledge distillation as a pathway toward next-generation intelligent ecohydrological modeling systems |
| topic | Machine Learning Geophysics |
| url | https://arxiv.org/abs/2509.01972 |