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View article: An Explainable Bayesian TimesNet for Probabilistic Groundwater Level Prediction
An Explainable Bayesian TimesNet for Probabilistic Groundwater Level Prediction Open
Reliable groundwater level (GWL) prediction is essential for sustainable water resources management. Despite recent advances in machine learning (ML) methods for GWL prediction, further improvements may be made in uncertainty quantificatio…
View article: AutoSGNN: Automatic Propagation Mechanism Discovery for Spectral Graph Neural Networks
AutoSGNN: Automatic Propagation Mechanism Discovery for Spectral Graph Neural Networks Open
In real-world applications, spectral Graph Neural Networks (GNNs) are powerful tools for processing diverse types of graphs. However, a single GNN often struggles to handle different graph types—such as homogeneous and heterogeneous graphs…
View article: Near‐Real‐Time Monitoring of Global Terrestrial Water Storage Anomalies and Hydrological Droughts
Near‐Real‐Time Monitoring of Global Terrestrial Water Storage Anomalies and Hydrological Droughts Open
Global terrestrial water storage anomaly (TWSA) products from the Gravity Recovery and Climate Experiment (GRACE) and its Follow‐On mission (GRACE/FO) have an approximately three‐month latency, significantly limiting their operational use …
View article: AutoSGNN: Automatic Propagation Mechanism Discovery for Spectral Graph Neural Networks
AutoSGNN: Automatic Propagation Mechanism Discovery for Spectral Graph Neural Networks Open
In real-world applications, spectral Graph Neural Networks (GNNs) are powerful tools for processing diverse types of graphs. However, a single GNN often struggles to handle different graph types-such as homogeneous and heterogeneous graphs…
View article: Global Water Monitor 2023, Summary Report
Global Water Monitor 2023, Summary Report Open
Record temperatures across most of the world in 2023 also affected water resources and water-related hazards. Heatwaves contributed to deepening and new droughts in South America and Canada. There were many extreme rainfall events, includi…
View article: Terrestrial water storage deficits during the GRACE and GRACE-FO gap
Terrestrial water storage deficits during the GRACE and GRACE-FO gap Open
This datasets provide predictions of the terrestrial water storage anomalies (TWSAs) during the 11-month gap (July 2017-May 2018) between the GRACE satellite and its follow-on GRACE-FO. The predictions were obtained by a hydroclimatic data…
View article: Terrestrial water storage deficits during the GRACE and GRACE-FO gap
Terrestrial water storage deficits during the GRACE and GRACE-FO gap Open
This datasets provide predictions of the terrestrial water storage anomalies (TWSAs) during the 11-month gap (July 2017-May 2018) between the GRACE satellite and its follow-on GRACE-FO. The predictions were obtained by a hydroclimatic data…
View article: At the Traffic Intersection, Stopping, or Walking? Pedestrian Path Prediction Based on KPOF-GPDM for Driving Assistance
At the Traffic Intersection, Stopping, or Walking? Pedestrian Path Prediction Based on KPOF-GPDM for Driving Assistance Open
Pedestrian detection has always been a research hotspot in the Advanced Driving Assistance System (ADAS) with great progress in recent years. However, for the ADAS, we not only need to detect the behavior of pedestrians in front of the veh…
View article: Predictions of the GRACE-derived terrestrial water storage anomalies generated by a Bayesian convolutional neural network
Predictions of the GRACE-derived terrestrial water storage anomalies generated by a Bayesian convolutional neural network Open
This dataset provides predictions of the GRACE-derived terrestrial water storage anomalies (TWSAs) to fill the approximately one-year gap (July 2017-May 2018) between GRACE and its successor GRACE Follow-On. The predictions were obtained b…
View article: A Bayesian convolutional neural network for prediction of GRACE-derived terrestrial water storage anomalies
A Bayesian convolutional neural network for prediction of GRACE-derived terrestrial water storage anomalies Open
This dataset provides predictions of the GRACE-derived terrestrial water storage anomalies to fill the approximately one-year gap (July 2017-May 2018) between GRACE and its successor GRACE Follow-On. The predictions were obtained by a Baye…
View article: Predictions of the GRACE-derived terrestrial water storage anomalies generated by a Bayesian convolutional neural network
Predictions of the GRACE-derived terrestrial water storage anomalies generated by a Bayesian convolutional neural network Open
This dataset provides predictions of the GRACE-derived terrestrial water storage anomalies (TWSAs) to fill the approximately one-year gap (July 2017-May 2018) between GRACE and its successor GRACE Follow-On. The predictions were obtained b…
View article: Improving prediction of the terrestrial water storage anomalies during the GRACE and GRACE-FO gap with Bayesian convolutional neural networks
Improving prediction of the terrestrial water storage anomalies during the GRACE and GRACE-FO gap with Bayesian convolutional neural networks Open
The Gravity Recovery and Climate Experiment (GRACE) satellite and its successor GRACE Follow-On (GRACE-FO) provide valuable and accurate observations of terrestrial water storage anomalies (TWSAs) at a global scale. However, there is an ap…
View article: Integration of Adversarial Autoencoders With Residual Dense Convolutional Networks for Estimation of Non‐Gaussian Hydraulic Conductivities
Integration of Adversarial Autoencoders With Residual Dense Convolutional Networks for Estimation of Non‐Gaussian Hydraulic Conductivities Open
Inverse modeling for the estimation of non‐Gaussian hydraulic conductivity fields in subsurface flow and solute transport models remains a challenging problem. This is mainly due to the non‐Gaussian property, the nonlinear physics, and the…
View article: Integration of adversarial autoencoders with residual dense convolutional networks for inversion of solute transport in non-Gaussian conductivity fields
Integration of adversarial autoencoders with residual dense convolutional networks for inversion of solute transport in non-Gaussian conductivity fields Open
Characterization of a non-Gaussian channelized conductivity field in subsurface flow and transport modeling through inverse modeling usually leads to a high-dimensional inverse problem and requires repeated evaluations of the forward model…
View article: A Taylor Expansion‐Based Adaptive Design Strategy for Global Surrogate Modeling With Applications in Groundwater Modeling
A Taylor Expansion‐Based Adaptive Design Strategy for Global Surrogate Modeling With Applications in Groundwater Modeling Open
Global sensitivity analysis (GSA) and uncertainty quantification (UQ) for groundwater modeling are challenging because of the model complexity and significant computational requirements. To reduce the massive computational cost, a cheap‐to…