Systematization of short-term forecasts of regional wave heights using a machine learning technique and long-term wave hindcast
Abstract Machine-learning techniques have been applied to wave forecasting to address the
implementational and computational challenges of conventional numerical wave models …
implementational and computational challenges of conventional numerical wave models …
A deep learning modeling framework with uncertainty quantification for inflow-outflow predictions for cascade reservoirs
Accurate prediction of reservoir inflows and outflows and their uncertainties is essential for
managing water resources and establishing early-warning systems. However, this can be a …
managing water resources and establishing early-warning systems. However, this can be a …
Closing in on Hydrologic Predictive Accuracy: Combining the Strengths of High‐Fidelity and Physics‐Agnostic Models
Applications of process‐based models (PBM) for predictions are confounded by multiple
uncertainties and computational burdens, resulting in appreciable errors. A novel modeling …
uncertainties and computational burdens, resulting in appreciable errors. A novel modeling …
Robust and efficient uncertainty quantification for extreme events that deviate significantly from the training dataset using polynomial chaos-kriging
This study presents the strengths of polynomial chaos-kriging (PCK), a new surrogate model
that merges polynomial chaos extension (PCE) and Gaussian process with kriging variance …
that merges polynomial chaos extension (PCE) and Gaussian process with kriging variance …
Development of a stochastic hydrological modeling system for improving ensemble streamflow prediction
Streamflow prediction plays a crucial role in water resources systems planning and the
mitigation of hydrological extremes such as floods and droughts. Since a variety of …
mitigation of hydrological extremes such as floods and droughts. Since a variety of …
Future projections and uncertainties of CMIP6 for hydrological indicators and their discrepancies from CMIP5 over South Korea
MV Doi, J Kim - Water, 2022 - mdpi.com
Future climate projections and their uncertainties affect many aspects of the world, so
reliable assessments are essential for policymakers who need to prepare mitigation …
reliable assessments are essential for policymakers who need to prepare mitigation …
Machine learning modeling structures and framework for short-term forecasting and long-term projection of Streamflow
Reliable short-term forecasting and long-term projection of streamflow are essential.
However, few research models for machine learning structures systematized for short-and …
However, few research models for machine learning structures systematized for short-and …
UIDS: A Matlab-based urban flood model considering rainfall-induced and surcharge-induced inundations
Abstract The Urban Inundation-Drainage Simulator (UIDS) is a new coupled model for
simulating urban flooding dynamics, developed as an open-source, MATLAB-based …
simulating urban flooding dynamics, developed as an open-source, MATLAB-based …
Reconstructing Long-Term Daily Streamflow Data at the Discontinuous Monitoring Station in the Ungauged Transboundary Basin Using Machine Learning
VN Tran, HD Nguyen, H Van Khuong, HB Dao… - Water Resources …, 2025 - Springer
Streamflow data is essential for water resource management, especially in transboundary
river basins where data sharing between countries is often limited. Simulating and …
river basins where data sharing between countries is often limited. Simulating and …
Encoder–Decoder Convolutional Neural Networks for Flow Modeling in Unsaturated Porous Media: Forward and Inverse Approaches
The computational cost of approximating the Richards equation for water flow in unsaturated
porous media is a major challenge, especially for tasks that require repetitive simulations …
porous media is a major challenge, especially for tasks that require repetitive simulations …