Abhilash Singh
@abhilashsingh.bsky.social
University of Leeds|[email protected]|Machine Learning|Artificial Intelligence|Hydrology|Remote Sensing|Codes: https://abhilashsingh.net/codes.html
Can weak physics improve machine-learning generalization to new (or any) sites compared to hard-constraint physics-informed machine learning that requires site-specific details?
We address this question in our new paper in GRL.
doi.org/10.1029/2025...
#soilmoisture #machinelearning
We address this question in our new paper in GRL.
doi.org/10.1029/2025...
#soilmoisture #machinelearning
October 18, 2025 at 8:38 AM
Can weak physics improve machine-learning generalization to new (or any) sites compared to hard-constraint physics-informed machine learning that requires site-specific details?
We address this question in our new paper in GRL.
doi.org/10.1029/2025...
#soilmoisture #machinelearning
We address this question in our new paper in GRL.
doi.org/10.1029/2025...
#soilmoisture #machinelearning
Reposted by Abhilash Singh
Hydrology Paper of the Day @mlearthsciences.bsky.social on the use of frequency-domain neural networks for soil moisture imputation: weather station observations and model application by sliding windows and spatial convolution; a comparison of approaches; and rainfall magnitudes and sensitivities.
Worried about missing value?
A novel imputation framework based on Fourier neural operators (FNO) for soil moisture. The FNO model outperforms traditional approaches, and incorporating temporal lag reduces error by up to 15% in the diverse climates in India and Zambia.
doi.org/10.1029/2025...
A novel imputation framework based on Fourier neural operators (FNO) for soil moisture. The FNO model outperforms traditional approaches, and incorporating temporal lag reduces error by up to 15% in the diverse climates in India and Zambia.
doi.org/10.1029/2025...
Leveraging Neural Operator and Sliding Window Technique for Enhanced Subsurface Soil Moisture Imputation Under Diverse Precipitation Scenarios
Developed a novel Fourier Neural Operator (FNO) to enhance subsurface soil moisture imputation by employing a sliding window concept that seamlessly integrates rainfall, soil temperature, and norm...
doi.org
September 14, 2025 at 4:07 AM
Hydrology Paper of the Day @mlearthsciences.bsky.social on the use of frequency-domain neural networks for soil moisture imputation: weather station observations and model application by sliding windows and spatial convolution; a comparison of approaches; and rainfall magnitudes and sensitivities.
First post !!!
Happy to share our new paper is out in Engineering Applications of AI!
"Overcoming Data Scarcity" uses transfer learning + satellite fusion to predict soil moisture with 55% less in-situ data
🔗 doi.org/10.1016/j.enga…
�� abhilashsingh.net/codes.ht#RemoteSensinge#MLn#AIML #AI
Happy to share our new paper is out in Engineering Applications of AI!
"Overcoming Data Scarcity" uses transfer learning + satellite fusion to predict soil moisture with 55% less in-situ data
🔗 doi.org/10.1016/j.enga…
�� abhilashsingh.net/codes.ht#RemoteSensinge#MLn#AIML #AI
July 22, 2025 at 11:03 PM
First post !!!
Happy to share our new paper is out in Engineering Applications of AI!
"Overcoming Data Scarcity" uses transfer learning + satellite fusion to predict soil moisture with 55% less in-situ data
🔗 doi.org/10.1016/j.enga…
�� abhilashsingh.net/codes.ht#RemoteSensinge#MLn#AIML #AI
Happy to share our new paper is out in Engineering Applications of AI!
"Overcoming Data Scarcity" uses transfer learning + satellite fusion to predict soil moisture with 55% less in-situ data
🔗 doi.org/10.1016/j.enga…
�� abhilashsingh.net/codes.ht#RemoteSensinge#MLn#AIML #AI