Ensemble machine learning paradigms in hydrology: A review
Recently, there has been a notable tendency towards employing ensemble learning
methodologies in assorted areas of engineering, such as hydrology, for simulation and …
methodologies in assorted areas of engineering, such as hydrology, for simulation and …
Assessment of soft computing techniques for the prediction of compressive strength of bacterial concrete
In this investigation, the potential of M5P, Random Tree (RT), Reduced Error Pruning Tree
(REP Tree), Random Forest (RF), and Support Vector Regression (SVR) techniques have …
(REP Tree), Random Forest (RF), and Support Vector Regression (SVR) techniques have …
Forecasting of stage-discharge in a non-perennial river using machine learning with gamma test
Abstract Knowledge of the stage-discharge rating curve is useful in designing and planning
flood warnings; thus, developing a reliable stage-discharge rating curve is a fundamental …
flood warnings; thus, developing a reliable stage-discharge rating curve is a fundamental …
Probabilistic slope stability analysis using subset simulation enhanced by ensemble machine learning techniques
Within the field of geotechnical engineering, complex challenges arise due to uncertainties
associated with variable loads, soil properties, ground stratification, and other related …
associated with variable loads, soil properties, ground stratification, and other related …
Discharge estimation using brink depth over a trapezoidal-shaped weir
A weir is an accurate hydraulic structure for estimating water discharge in open channels
and rivers. It can be constructed with a standard shape with vertical upstream and …
and rivers. It can be constructed with a standard shape with vertical upstream and …
Modeling of scour depth and length of a diversion channel flow system with soft computing techniques
This study employed soft computing techniques, namely, support vector machine (SVM) and
Gaussian process regression (GPR) techniques, to predict the properties of a scour hole …
Gaussian process regression (GPR) techniques, to predict the properties of a scour hole …
[HTML][HTML] Prediction of seepage flow through earthfill dams using machine learning models
In this study, three machine learning models, namely, the Multilayer Perceptron Neural
Networks (MLPNN), the Generalized Regression Neural Networks (GRNN) and the Radial …
Networks (MLPNN), the Generalized Regression Neural Networks (GRNN) and the Radial …
Application of remote sensing and GIS techniques for monitoring water volume variations in inaccessible reservoirs
MMA Albayati, AMS Al-Janabi… - Hydrological Sciences …, 2024 - Taylor & Francis
Change detection processes using remote sensing and Geographic Information System
(GIS) techniques were applied to monitor and estimate the water volume in the Mosul Dam …
(GIS) techniques were applied to monitor and estimate the water volume in the Mosul Dam …
Comparative assessment of advanced machine learning techniques for simulation of lake water level fluctuations based on different dimensionality reduction methods
Global warming and unprecedented human impacts causing environmental degradation are
taking place at an alarming rate. As one of the most valuable and important water resources …
taking place at an alarming rate. As one of the most valuable and important water resources …
A novel hybrid approach based on outlier and error correction methods to predict river discharge using meteorological variables
A new hybrid approach for the river discharge prediction is proposed by integrating the
Hampel filter (HF) with an autoregressive distributed lag (ARDL) model and multi-model …
Hampel filter (HF) with an autoregressive distributed lag (ARDL) model and multi-model …