Deep learning in hydrology and water resources disciplines: Concepts, methods, applications, and research directions
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 …
What role does hydrological science play in the age of machine learning?
This paper is derived from a keynote talk given at the Google's 2020 Flood Forecasting
Meets Machine Learning Workshop. Recent experiments applying deep learning to rainfall …
Meets Machine Learning Workshop. Recent experiments applying deep learning to rainfall …
From hydrometeorology to river water quality: can a deep learning model predict dissolved oxygen at the continental scale?
Dissolved oxygen (DO) reflects river metabolic pulses and is an essential water quality
measure. Our capabilities of forecasting DO however remain elusive. Water quality data …
measure. Our capabilities of forecasting DO however remain elusive. Water quality data …
Enhancing streamflow forecast and extracting insights using long‐short term memory networks with data integration at continental scales
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 …
regularly spaced) can help to improve streamflow forecasts, but it is challenging to integrate …
[PDF][PDF] Deep learning based modeling of groundwater storage change
The understanding of water resource changes and a proper projection of their future
availability are necessary elements of sustainable water planning. Monitoring GWS change …
availability are necessary elements of sustainable water planning. Monitoring GWS change …
[PDF][PDF] CDLSTM: A novel model for climate change forecasting.
MA Haq - Computers, Materials & Continua, 2022 - researchgate.net
Water received in rainfall is a crucial natural resource for agriculture, the hydrological cycle,
and municipal purposes. The changing rainfall pattern is an essential aspect of assessing …
and municipal purposes. The changing rainfall pattern is an essential aspect of assessing …
[HTML][HTML] Hybrid forecasting: blending climate predictions with AI models
Hybrid hydroclimatic forecasting systems employ data-driven (statistical or machine
learning) methods to harness and integrate a broad variety of predictions from dynamical …
learning) methods to harness and integrate a broad variety of predictions from dynamical …
Machine learning for hydrologic sciences: An introductory overview
The hydrologic community has experienced a surge in interest in machine learning in recent
years. This interest is primarily driven by rapidly growing hydrologic data repositories, as …
years. This interest is primarily driven by rapidly growing hydrologic data repositories, as …
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 …