Spatial Transfer Learning for Estimating PM2.5 in Data-poor Regions
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866916296909651968 |
|---|---|
| author | Gupta, Shrey Park, Yongbee Bi, Jianzhao Gupta, Suyash Züfle, Andreas Wildani, Avani Liu, Yang |
| author_facet | Gupta, Shrey Park, Yongbee Bi, Jianzhao Gupta, Suyash Züfle, Andreas Wildani, Avani Liu, Yang |
| contents | Air pollution, especially particulate matter 2.5 (PM2.5), is a pressing concern for public health and is difficult to estimate in developing countries (data-poor regions) due to a lack of ground sensors. Transfer learning models can be leveraged to solve this problem, as they use alternate data sources to gain knowledge (i.e., data from data-rich regions). However, current transfer learning methodologies do not account for dependencies between the source and the target domains. We recognize this transfer problem as spatial transfer learning and propose a new feature named Latent Dependency Factor (LDF) that captures spatial and semantic dependencies of both domains and is subsequently added to the feature spaces of the domains. We generate LDF using a novel two-stage autoencoder model that learns from clusters of similar source and target domain data. Our experiments show that transfer learning models using LDF have a 19.34% improvement over the baselines. We additionally support our experiments with qualitative findings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_07308 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Spatial Transfer Learning for Estimating PM2.5 in Data-poor Regions Gupta, Shrey Park, Yongbee Bi, Jianzhao Gupta, Suyash Züfle, Andreas Wildani, Avani Liu, Yang Machine Learning Air pollution, especially particulate matter 2.5 (PM2.5), is a pressing concern for public health and is difficult to estimate in developing countries (data-poor regions) due to a lack of ground sensors. Transfer learning models can be leveraged to solve this problem, as they use alternate data sources to gain knowledge (i.e., data from data-rich regions). However, current transfer learning methodologies do not account for dependencies between the source and the target domains. We recognize this transfer problem as spatial transfer learning and propose a new feature named Latent Dependency Factor (LDF) that captures spatial and semantic dependencies of both domains and is subsequently added to the feature spaces of the domains. We generate LDF using a novel two-stage autoencoder model that learns from clusters of similar source and target domain data. Our experiments show that transfer learning models using LDF have a 19.34% improvement over the baselines. We additionally support our experiments with qualitative findings. |
| title | Spatial Transfer Learning for Estimating PM2.5 in Data-poor Regions |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2404.07308 |