Now showing items 1-6 of 6
Flood hydrograph prediction using machine learning methods
Machine learning (soft) methods have a wide range of applications in many disciplines, including hydrology. The first application of these methods in hydrology started in the 1990s and have since been extensively employed. ...
Coupling soil moisture and precipitation observations for predicting hourly runoff at small catchment scale
The importance of soil moisture is recognized in rainfall-runoff processes. This study quantitatively investigates the use of soil moisture measured at 10, 20, and 40cm soil depths along with rainfall in predicting runoff. ...
Predicting and forecasting flow discharge at sites receiving significant lateral inflow
Two models, one linear and one non-linear, were employed for the prediction of flow discharge hydrographs at sites receiving significant lateral inflow. The linear model is based on a rating curve and permits a quick ...
Predicting hourly-based flow discharge hydrographs from level data using genetic algorithms
This study developed a genetic algorithm model to predict flow rates at sites receiving significant lateral inflow. It predicts flow rate at a downstream station from flow stage measured at upstream and downstream stations. ...
Reverse flood routing in natural channels using genetic algorithm
Establishing a clear overview of data discharge availability for water balance modelling in basins is a priority in Europe, and in the particular in the framework of the system of Economic and Environmental Accounts for ...
Genetic algorithm-based discharge estimation at sites receiving lateral inflows
(American Society of Civil Engineers, 2009)
The genetic algorithm (GA) technique is applied to obtain optimal parameter values of the standard rating curve model (RCM) for predicting, in real time, event-based flow discharge hydrographs at sites receiving significant ...