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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2507.21486 |
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| _version_ | 1866913964584075264 |
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| author | Kumabe, Satoshi Song, Tianyu Ta, Ton Viet |
| author_facet | Kumabe, Satoshi Song, Tianyu Ta, Ton Viet |
| contents | Forest transitions, characterized by dynamic shifts between forest, agricultural, and abandoned lands, are complex phenomena. This study developed a stochastic differential equation model to capture the intricate dynamics of these transitions. We established the existence of global positive solutions for the model and conducted numerical analyses to assess the impact of model parameters on deforestation incentives. To address the challenge of parameter estimation, we proposed a novel deep learning approach that estimates all model parameters from a single sample containing time-series observations of forest and agricultural land proportions. This innovative approach enables us to understand forest transition dynamics and deforestation trends at any future time. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_21486 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Stochastic forest transition model dynamics and parameter estimation via deep learning Kumabe, Satoshi Song, Tianyu Ta, Ton Viet Machine Learning Forest transitions, characterized by dynamic shifts between forest, agricultural, and abandoned lands, are complex phenomena. This study developed a stochastic differential equation model to capture the intricate dynamics of these transitions. We established the existence of global positive solutions for the model and conducted numerical analyses to assess the impact of model parameters on deforestation incentives. To address the challenge of parameter estimation, we proposed a novel deep learning approach that estimates all model parameters from a single sample containing time-series observations of forest and agricultural land proportions. This innovative approach enables us to understand forest transition dynamics and deforestation trends at any future time. |
| title | Stochastic forest transition model dynamics and parameter estimation via deep learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2507.21486 |