A review of hybrid deep learning applications for streamflow forecasting
Deep learning has emerged as a powerful tool for streamflow forecasting and its
applications have garnered significant interest in the hydrological community. Despite the …
applications have garnered significant interest in the hydrological community. Despite the …
Forecast of rainfall distribution based on fixed sliding window long short-term memory
Applying data mining techniques for rainfall modeling because of a lack of sufficient memory
components may increase uncertainty in rainfall forecasting. To solve this issue, in this …
components may increase uncertainty in rainfall forecasting. To solve this issue, in this …
[HTML][HTML] Using a long short-term memory (LSTM) neural network to boost river streamflow forecasts over the western United States
KMR Hunt, GR Matthews… - Hydrology and Earth …, 2022 - hess.copernicus.org
Accurate river streamflow forecasts are a vital tool in the fields of water security, flood
preparation and agriculture, as well as in industry more generally. Traditional physics-based …
preparation and agriculture, as well as in industry more generally. Traditional physics-based …
Deep transfer learning based on transformer for flood forecasting in data-sparse basins
There exists a substantial disparity in the distribution of streamflow gauge and basin
characteristic information, with a majority of flood observations being recorded from a limited …
characteristic information, with a majority of flood observations being recorded from a limited …
Transferring hydrologic data across continents–leveraging data‐rich regions to improve hydrologic prediction in data‐sparse regions
There is a drastic geographic imbalance in available global streamflow gauge and
catchment property data, with additional large variations in data characteristics. As a result …
catchment property data, with additional large variations in data characteristics. As a result …
[HTML][HTML] Machine-learning methods for stream water temperature prediction
M Feigl, K Lebiedzinski, M Herrnegger… - Hydrology and Earth …, 2021 - hess.copernicus.org
Water temperature in rivers is a crucial environmental factor with the ability to alter hydro-
ecological as well as socio-economic conditions within a catchment. The development of …
ecological as well as socio-economic conditions within a catchment. The development of …
Mitigating prediction error of deep learning streamflow models in large data‐sparse regions with ensemble modeling and soft data
Predicting discharge in contiguously data‐scarce or ungauged regions is needed for
quantifying the global hydrologic cycle. We show that prediction in ungauged regions (PUR) …
quantifying the global hydrologic cycle. We show that prediction in ungauged regions (PUR) …
Influence of climate and land-use changes on the sensitivity of SWAT model parameters and water availability in a semi-arid river basin
The present study assesses the impact of climate change (CC) and land use land cover
change (LULCC) on model parameter variability and alterations in streamflow and water …
change (LULCC) on model parameter variability and alterations in streamflow and water …
[HTML][HTML] Comprehensive comparison of artificial neural networks and long short-term memory networks for rainfall-runoff simulation
Accurate and efficient runoff simulations are crucial for water management in basins.
Rainfall-runoff simulation approaches range between physical, conceptual, and data-driven …
Rainfall-runoff simulation approaches range between physical, conceptual, and data-driven …
A novel smoothing-based deep learning time-series approach for daily suspended sediment load prediction
Precise assessment of suspended sediment load (SSL) is vital for many applications in
hydrological modeling and hydraulic engineering. In this study, a smoothed long short-term …
hydrological modeling and hydraulic engineering. In this study, a smoothed long short-term …