Deep learning in hydrology and water resources disciplines: Concepts, methods, applications, and research directions
KP Tripathy, AK Mishra - Journal of Hydrology, 2024 - Elsevier
Over the past few years, Deep Learning (DL) methods have garnered substantial
recognition within the field of hydrology and water resources applications. Beginning with a …
recognition within the field of hydrology and water resources applications. Beginning with a …
Differentiable modelling to unify machine learning and physical models for geosciences
Process-based modelling offers interpretability and physical consistency in many domains of
geosciences but struggles to leverage large datasets efficiently. Machine-learning methods …
geosciences but struggles to leverage large datasets efficiently. Machine-learning methods …
Differentiable, learnable, regionalized process‐based models with multiphysical outputs can approach state‐of‐the‐art hydrologic prediction accuracy
Predictions of hydrologic variables across the entire water cycle have significant value for
water resources management as well as downstream applications such as ecosystem and …
water resources management as well as downstream applications such as ecosystem and …
[HTML][HTML] The suitability of differentiable, physics-informed machine learning hydrologic models for ungauged regions and climate change impact assessment
As a genre of physics-informed machine learning, differentiable process-based hydrologic
models (abbreviated as δ or delta models) with regionalized deep-network-based …
models (abbreviated as δ or delta models) with regionalized deep-network-based …
Improving river routing using a differentiable Muskingum‐Cunge model and physics‐informed machine learning
Recently, rainfall‐runoff simulations in small headwater basins have been improved by
methodological advances such as deep neural networks (NNs) and hybrid physics‐NN …
methodological advances such as deep neural networks (NNs) and hybrid physics‐NN …
Optimizing Irrigation Efficiency with IoT and Machine Learning: A Transfer Learning Approach for Accurate Soil Moisture Prediction
This research aims to develop a Machine Learning model for predicting soil moisture levels,
which may be used to construct smart irrigation systems. The model was evaluated and …
which may be used to construct smart irrigation systems. The model was evaluated and …
Inter-comparison and integration of different soil moisture downscaling methods over the Qinghai-Tibet Plateau
Y Shangguan, X Min, Z Shi - Journal of Hydrology, 2023 - Elsevier
Soil moisture (SM) is a key state variable in the water, energy cycle between atmosphere
and land surface but existing passive microwave soil moisture products typically have …
and land surface but existing passive microwave soil moisture products typically have …
The data synergy effects of time‐series deep learning models in hydrology
When fitting statistical models to variables in geoscientific disciplines such as hydrology, it is
a customary practice to stratify a large domain into multiple regions (or regimes) and study …
a customary practice to stratify a large domain into multiple regions (or regimes) and study …
Short-and mid-term forecasts of actual evapotranspiration with deep learning
Evapotranspiration is a key component of the hydrologic cycle. Accurate short-, medium-,
and long-term forecasts of actual evapotranspiration (ET a) are crucial not only for …
and long-term forecasts of actual evapotranspiration (ET a) are crucial not only for …
Integration of deep learning and information theory for designing monitoring networks in heterogeneous aquifer systems
Groundwater monitoring networks are direct sources of information for revealing subsurface
system dynamic processes. However, designing such networks is difficult due to …
system dynamic processes. However, designing such networks is difficult due to …