We used three machine learning models namely artificial neural networks, model tree, locally weighted regression to predict output uncertainties.
These three models produce similar verification results, which can be improved by merging their outputs dynamically. We propose an approach to form a committee of the three models to merge their outputs. The approach is applied to estimate uncertainty of streamflows simulation from a conceptual hydrological model in the Brue catchment in UK and the Bagmati catchment in Nepal.
The verification results show that merged output is better than an individual model output. IWA; Skip to Main Content.
Select Print. They have been recognized as very powerful tools in capturing simple as well as complex functional relationships between several input and output variables.
Many studies have shown the potential of ANNs for modeling rainfall-runoff relationships over watersheds and comprise most ANN applications today. Unfortunately there is a lack of computer applications for hydrological modeling using ANNs with a user-friendly interface.
- Neural Networks for Hydrological Modeling - CRC Press Book?
- Neural Networks for Hydrological Modelling by Robert J. Abrahart.
- The Fourth Dimension: Toward a Geometry of Higher Reality.
- The Indian Ocean in World History (Themes in World History)?
- The Rio Grande.
In this study, we developed HydroNeuralNetworks, a user-friendly software tool developed for hydrological modeling that runs under Matlab a and above. The software uses a multilayer feedforward network.
This architecture can approximate any nonlinear function. Feedforward networks consist of a series of layers where the first layer has a connection from the network input. Each subsequent layer has a connection from the previous layer. The final layer produces the network's output.