تقرير
Causal Modeling of Soil Processes for Improved Generalization
| العنوان: | Causal Modeling of Soil Processes for Improved Generalization |
|---|---|
| المؤلفون: | Sharma, Somya, Sharma, Swati, Neal, Andy, Malvar, Sara, Rodrigues, Eduardo, Crawford, John, Kiciman, Emre, Chandra, Ranveer |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Computer Science - Computers and Society |
| الوصف: | Measuring and monitoring soil organic carbon is critical for agricultural productivity and for addressing critical environmental problems. Soil organic carbon not only enriches nutrition in soil, but also has a gamut of co-benefits such as improving water storage and limiting physical erosion. Despite a litany of work in soil organic carbon estimation, current approaches do not generalize well across soil conditions and management practices. We empirically show that explicit modeling of cause-and-effect relationships among the soil processes improves the out-of-distribution generalizability of prediction models. We provide a comparative analysis of soil organic carbon estimation models where the skeleton is estimated using causal discovery methods. Our framework provide an average improvement of 81% in test mean squared error and 52% in test mean absolute error. Comment: NeurIPS 2022 Workshop Tackling Climate Change with Machine Learning |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2211.05675 |
| رقم الأكسشن: | edsarx.2211.05675 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |