553 citations · 1.6k across the 6 of their papers we have counts for
6 papers
Identifying Trustworthiness Challenges in Deep Learning Models for Continental-Scale Water Quality Prediction
Xiaobo Xia, Xiaofeng Liu, Jiale Liu +5
Water quality is foundational to environmental sustainability, ecosystem resilience, and public health. Deep learning offers transformative potential for large-scale water quality…
Differentiable modeling to unify machine learning and physical models and advance Geosciences
Chaopeng Shen, Alison P. Appling, Pierre Gentine +27
Process-Based Modeling (PBM) and Machine Learning (ML) are often perceived as distinct paradigms in the geosciences. Here we present differentiable geoscientific modeling as a powe…
The data synergy effects of time-series deep learning models in hydrology
Kuai Fang, Daniel Kifer, Kathryn Lawson +2
When fitting statistical models to variables in geoscientific disciplines such as hydrology, it is a customary practice to regionalize - to divide a large spatial domain into multi…
Enhancing streamflow forecast and extracting insights using long-short term memory networks with data integration at continental scales
Dapeng Feng, Kuai Fang, Chaopeng Shen
Recent observations with varied schedules and types (moving average, snapshot, or regularly spaced) can help to improve streamflow forecasts, but it is challenging to integrate the…
Evaluating aleatoric and epistemic uncertainties of time series deep learning models for soil moisture predictions
Kuai Fang, Chaopeng Shen, Daniel Kifer
Soil moisture is an important variable that determines floods, vegetation health, agriculture productivity, and land surface feedbacks to the atmosphere, etc. Accurately modeling s…
Prolongation of SMAP to Spatio-temporally Seamless Coverage of Continental US Using a Deep Learning Neural Network
Kuai Fang, Chaopeng Shen, Daniel Kifer +1
The Soil Moisture Active Passive (SMAP) mission has delivered valuable sensing of surface soil moisture since 2015. However, it has a short time span and irregular revisit schedule…