The support vector machine is used as a data mining technique to extract informative hydrologic data on the basis of a strong relationship between error tolerance and the number of support vectors. Hydrologic data of flash flood events in the Lan-Yang River basin in Taiwan were used for the case study. Various percentages (from 50% to 10%) of hydrologic data, including those for flood stage and...

“Hydromax” is the river flow forecasting system for the early warning of extreme hydrological events (floods as well as low waters) in the Meuse river basin and its tributaries. Hydromax provides in real-time short-term predictions of river flows based on past rainfall and river flow measurements, and long-term flood forecasting based on meteorological forecasts. It is now successfully in routi...

Hydrological forecasting can provide the basis for dealing with the conflict between flood control and water use. This paper proposes a flood and sediment forecasting method for rivers by deriving an energy equation which describes the conservation of potential slope, kinetic slope, and friction slope of a river reach. The equation contains an additional term, the rate of change of stage, which...

Flood wave propagation in river channel flow can be enunciated by nonlinear equations of motion for unsteady flow. It is difficult to find analytical solution of these non-linear equations. Hence, in this paper verification of the finite element model has been carried out against available numerical predictions and field data. The results of the model indicate a good matching with both Preissma...

The nonlinearity and uncertainty of the flood process are such that estimating or predicting required hydrologic data is often tremendous difficult. Consequently, this study employs a Back-Propagation Network (BPN) as the main structure in flood forecasting to learn and demonstrate the sophisticated nonlinear mapping relationship. However, sophisticated natural systems and highly changeable hyd...

This paper assesses one of many potential enhancements to conventional flood forecasting that can be achieved through the use of soft computing technologies. A methodology is outlined in which the forecasting data set is split into subsets before training with a series of neural networks. These networks are then recombined via a rule-based fuzzy logic model that has been optimized using a genet...

A regional-scale weather model is used to determine the potential for flood forecasting based on modelpredicted rainfall. Extreme precipitation and flooding events are a significant concern in central Texas, due to both the high occurrence and severity of flooding in the area. However, many current regional prediction models do not provide sufficient accuracy at the watershed scale necessary fo...