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Found 18 papers, 6 papers with code
The geometry of flow: Advancing predictions of river geometry with multi-model machine learning
Hydraulic geometry parameters describing river hydrogeomorphic is important for flood forecasting.
Estimating Uncertainty in Landslide Segmentation Models
Landslides are a recurring, widespread hazard.
Probing the limit of hydrologic predictability with the Transformer network
While the Transformer results are not higher than current state-of-the-art, we still learned some valuable lessons: (1) the vanilla Transformer architecture is not suitable for hydrologic modeling; (2) the proposed recurrence-free modification can improve Transformer performance so future work can continue to test more of such modifications; and (3) the prediction limits on the dataset should be close to the current state-of-the-art model.
Differentiable modeling to unify machine learning and physical models and advance Geosciences
Here we present differentiable geoscientific modeling as a powerful pathway toward dissolving the perceived barrier between them and ushering in a paradigm shift.
PatchRefineNet: Improving Binary Segmentation by Incorporating Signals from Optimal Patch-wise Binarization
Given the logit scores produced by the base segmentation model, each pixel is given a pseudo-label that is obtained by optimally thresholding the logit scores in each image patch.
Ranked #1 on
Few-Shot Semantic Segmentation
on FSS-1000 (5-shot)
(mIoU metric)
Differentiable, learnable, regionalized process-based models with physical outputs can approach state-of-the-art hydrologic prediction accuracy
Without using an ensemble or post-processor, {\delta} models can obtain a median Nash Sutcliffe efficiency of 0. 732 for 671 basins across the USA for the Daymet forcing dataset, compared to 0. 748 from a state-of-the-art LSTM model with the same setup.
Bathymetry Inversion using a Deep-Learning-Based Surrogate for Shallow Water Equations Solvers
We also found the surrogate architecture (whether with both velocity and water surface elevation or velocity only as outputs) does not show significant impact on inversion result.
A Robust Statistical Analysis of the Role of Hydropower on the System Electricity Price and Price Volatility
Hydroelectric power (hydropower) is unique in that it can function as both a conventional source of electricity and as backup storage (pumped hydroelectric storage) for providing energy in times of high demand on the grid.
Surrogate Model for Shallow Water Equations Solvers with Deep Learning
The new method was evaluated and compared against existing methods based on convolutional neural networks (CNNs), which can only make image-to-image predictions on structured or regular meshes.
Critical Risk Indicators (CRIs) for the electric power grid: A survey and discussion of interconnected effects
We also provide relevant critical risk indicators (CRIs) across diverse domains that may influence electric power grid risks, including climate, ecology, hydrology, finance, space weather, and agriculture.
Applications
Continental-scale streamflow modeling of basins with reservoirs: towards a coherent deep-learning-based strategy
However, dammed basins must be present in the training dataset.
The data synergy effects of time-series deep learning models in hydrology
We hypothesize that DL models automatically adjust their internal representations to identify commonalities while also providing sufficient discriminatory information to the model.
Prediction in ungauged regions with sparse flow duration curves and input-selection ensemble modeling
While long short-term memory (LSTM) models have demonstrated stellar performance with streamflow predictions, there are major risks in applying these models in contiguous regions with no gauges, or predictions in ungauged regions (PUR) problems.
From calibration to parameter learning: Harnessing the scaling effects of big data in geoscientific modeling
The behaviors and skills of models in many geosciences (e. g., hydrology and ecosystem sciences) strongly depend on spatially-varying parameters that need calibration.
Enhancing streamflow forecast and extracting insights using long-short term memory networks with data integration at continental scales
DI was most beneficial in regions with high flow autocorrelation: it greatly reduced baseflow bias in groundwater-dominated western basins and also improved peak prediction for basins with dynamical surface water storage, such as the Prairie Potholes or Great Lakes regions.
Evaluating aleatoric and epistemic uncertainties of time series deep learning models for soil moisture predictions
Soil moisture is an important variable that determines floods, vegetation health, agriculture productivity, and land surface feedbacks to the atmosphere, etc.
A trans-disciplinary review of deep learning research for water resources scientists
I argue that DL can help address several major new and old challenges facing research in water sciences such as inter-disciplinarity, data discoverability, hydrologic scaling, equifinality, and needs for parameter regionalization.
Prolongation of SMAP to Spatio-temporally Seamless Coverage of Continental US Using a Deep Learning Neural Network
The Soil Moisture Active Passive (SMAP) mission has delivered valuable sensing of surface soil moisture since 2015.
