[HTML][HTML] A brief review of random forests for water scientists and practitioners and their recent history in water resources
Random forests (RF) is a supervised machine learning algorithm, which has recently started
to gain prominence in water resources applications. However, existing applications are …
to gain prominence in water resources applications. However, existing applications are …
[HTML][HTML] Machine learning in electron microscopy for advanced nanocharacterization: current developments, available tools and future outlook
In the last few years, electron microscopy has experienced a new methodological paradigm
aimed to fix the bottlenecks and overcome the challenges of its analytical workflow. Machine …
aimed to fix the bottlenecks and overcome the challenges of its analytical workflow. Machine …
[HTML][HTML] Improving streamflow prediction in the WRF-Hydro model with LSTM networks
Researchers have attempted to use machine learning algorithms to replace physically
based models for streamflow prediction. Although existing studies have contributed to …
based models for streamflow prediction. Although existing studies have contributed to …
Stacked machine learning algorithms and bidirectional long short-term memory networks for multi-step ahead streamflow forecasting: A comparative study
Prediction of river flow rates is an essential task for both flood protection and optimal water
resource management. The high uncertainty associated with basin characteristics …
resource management. The high uncertainty associated with basin characteristics …
Long lead-time daily and monthly streamflow forecasting using machine learning methods
Long lead-time streamflow forecasting is of great significance for water resources planning
and management in both the short and long terms. Despite of some studies using machine …
and management in both the short and long terms. Despite of some studies using machine …
Neuroforecasting of daily streamflows in the UK for short-and medium-term horizons: A novel insight
F Granata, F Di Nunno - Journal of Hydrology, 2023 - Elsevier
Predicting streamflows, which is crucial for flood defence and optimal management of water
resources for drinking, irrigation, hydropower generation and ecosystem conservation, is a …
resources for drinking, irrigation, hydropower generation and ecosystem conservation, is a …
Process‐guided deep learning predictions of lake water temperature
The rapid growth of data in water resources has created new opportunities to accelerate
knowledge discovery with the use of advanced deep learning tools. Hybrid models that …
knowledge discovery with the use of advanced deep learning tools. Hybrid models that …
Employing machine learning algorithms for streamflow prediction: a case study of four river basins with different climatic zones in the United States
Streamflow estimation plays a significant role in water resources management, especially for
flood mitigation, drought warning, and reservoir operation. Hence, the current study …
flood mitigation, drought warning, and reservoir operation. Hence, the current study …
Statistical downscaling of precipitation using machine learning techniques
Statistical models were developed for downscaling reanalysis data to monthly precipitation
at 48 observation stations scattered across the Australian State of Victoria belonging to wet …
at 48 observation stations scattered across the Australian State of Victoria belonging to wet …
Simulation and forecasting of streamflows using machine learning models coupled with base flow separation
Efficient simulation of rainfall-runoff relationships is one of the most complex problems owing
to the high number of interrelated hydrological processes. It is well-known that machine …
to the high number of interrelated hydrological processes. It is well-known that machine …