no code implementations • 13 Jan 2024 • Somya Sharma, Swati Sharma, Rafael Padilha, Emre Kiciman, Ranveer Chandra
Using the sensor data, we preserve underlying causal relations among sensor attributes and organic matter.
no code implementations • 15 Jun 2023 • Somya Sharma, Swati Sharma, Licheng Liu, Rishabh Tushir, Andy Neal, Robert Ness, John Crawford, Emre Kiciman, Ranveer Chandra
Process-based models and analyzing observed data provide two avenues for improving our understanding of soil processes.
no code implementations • 16 Feb 2023 • Rahul Ghosh, HaoYu Yang, Ankush Khandelwal, Erhu He, Arvind Renganathan, Somya Sharma, Xiaowei Jia, Vipin Kumar
However, these entity characteristics are not readily available in many real-world scenarios, and different ML methods have been proposed to infer these characteristics from the data.
no code implementations • 10 Nov 2022 • Somya Sharma, Swati Sharma, Andy Neal, Sara Malvar, Eduardo Rodrigues, John Crawford, Emre Kiciman, Ranveer Chandra
Measuring and monitoring soil organic carbon is critical for agricultural productivity and for addressing critical environmental problems.
no code implementations • 12 Oct 2022 • Somya Sharma, Rahul Ghosh, Arvind Renganathan, Xiang Li, Snigdhansu Chatterjee, John Nieber, Christopher Duffy, Vipin Kumar
We propose uncertainty based learning method that offers 6\% improvement in $R^2$ for streamflow prediction (forward modeling) from inverse model inferred basin characteristic estimates, 17\% reduction in uncertainty (40\% in presence of noise) and 4\% higher coverage rate for basin characteristics.
no code implementations • 26 Nov 2021 • Neel Chatterjee, Somya Sharma, Sarah Swisher, Snigdhansu Chatterjee
Using these TFT models to draw inference involves estimating parameters used to fit to the experimental data.