Assessing the simulation of streamflow with the LSTM model across the continental United States using the MOPEX dataset
This study aims to assess the spatiotemporal performance of Machine Learning-based
techniques for simulating streamflow on a continental scale using Long-Sort Term Memory …
techniques for simulating streamflow on a continental scale using Long-Sort Term Memory …
A Novel Physics‐Aware Machine Learning‐Based Dynamic Error Correction Model for Improving Streamflow Forecast Accuracy
Occurrences of extreme events, especially floods, have become more frequent and severe
in the recent past due to the global impacts of climate change. In this context, possibilities for …
in the recent past due to the global impacts of climate change. In this context, possibilities for …
[HTML][HTML] Improving the predictive skills of hydrological models using a combinatorial optimization algorithm and artificial neural networks
Ensemble modelling is a numerical technique used to combine the results of a number of
different individual models in order to obtain more robust, better-fitting predictions. The main …
different individual models in order to obtain more robust, better-fitting predictions. The main …
A Physics‐Aware Machine Learning‐Based Framework for Minimizing Prediction Uncertainty of Hydrological Models
Modeling hydrological processes for managing the available water resources effectively is
often complex due to the existence of high nonlinearity, and the associated prediction …
often complex due to the existence of high nonlinearity, and the associated prediction …
[HTML][HTML] Predicting daily streamflow in a cold climate using a novel data mining technique: Radial M5 Model Tree
In this study, the viability of radial M5 model tree (RM5Tree) is investigated in prediction and
estimation of daily streamflow in a cold climate. The RM5Tree model is compared with the …
estimation of daily streamflow in a cold climate. The RM5Tree model is compared with the …
[HTML][HTML] Long-Term Variability of the Hydrological Regime and Its Response to Climate Warming in the Zhizdra River Basin of the Eastern European Plain
Climate warming globally has a profound effect on the hydrological regime, amplifying
evapotranspiration and precipitation and accelerating the processes of snow melt and …
evapotranspiration and precipitation and accelerating the processes of snow melt and …
Spatio-temporal Deep Learning Model for Accurate Streamflow Prediction with Multi-source Data Fusion
Z Wang, N Xu, X Bao, J Wu, X Cui - Environmental Modelling & Software, 2024 - Elsevier
Accurately predicting streamflow and early flood warning are important but also very
challenging, due to the complexity and stochastic nature of the runoff process. By describing …
challenging, due to the complexity and stochastic nature of the runoff process. By describing …
Improving Bayesian model averaging for ensemble flood modeling using multiple Markov chains Monte Carlo sampling
As all kinds of numerical models are emerging in hydrologic and hydraulic engineering,
Bayesian model averaging (BMA) is one of the popular multi‐model methods used to …
Bayesian model averaging (BMA) is one of the popular multi‐model methods used to …
Hydrosedimentology of paired watersheds with clayey soils under cattle grazing and no-tillage cropping: LISEM calibration and validation
ÉD Ebling, I Althoff, JM Reichert - International Journal of Environmental …, 2024 - Springer
Hydrosedimentalogical models contribute to management of water resources, provided they
are based on robust monitoring and calibration–validation strategies. The Limburg Soil …
are based on robust monitoring and calibration–validation strategies. The Limburg Soil …
Uncertainty quantization of meteorological input and model parameters for hydrological modelling using a Bayesian‐based integrated approach
X Yan, J Song, Y An, W Lu - Hydrological Processes, 2024 - Wiley Online Library
The traditional treatment of uncertainty in hydrological modelling primarily attributes it to
model parameters, but rarely systematically considers meteorological input errors …
model parameters, but rarely systematically considers meteorological input errors …