Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
Environmental Modelling & Software
Non-linear variable selection for artificial neural networks using partial mutual information
Environmental Modelling & Software
Multi-objective calibration and fuzzy preference selection of a distributed hydrological model
Environmental Modelling & Software
A time series tool to support the multi-criteria performance evaluation of rainfall-runoff models
Environmental Modelling & Software
Including the influence of groundwater exchanges in a lumped rainfall-runoff model
Mathematics and Computers in Simulation
An introduction to the hydrological modelling system PREVAH and its pre- and post-processing-tools
Environmental Modelling & Software
Short communication: Sensitivity analysis in fuzzy systems: Integration of SimLab and DANA
Environmental Modelling & Software
Convergence and uncertainty analyses in Monte-Carlo based sensitivity analysis
Environmental Modelling & Software
A DSS generator for multiobjective optimisation of spreadsheet-based models
Environmental Modelling & Software
An open software environment for hydrological model assessment and development
Environmental Modelling & Software
Improving PSO-Based multi-objective optimization using crowding, mutation and ∈-dominance
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Environmental Modelling & Software
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Regionalization of rainfall-runoff models is required for many catchments, where a suitable flow record is not available to enable traditional calibration methods to be used. Most recently, donor catchment approaches have been identified as the most successful at providing suitable model parameter values. However, this approach is less attractive for regions where the number of suitable catchments available to derive model parameters is low. In this case, regression approaches that consider catchment characteristics available in GIS databases may be more appropriate. Approaches such as this have been criticized due to issues associated with the ability to identify suitable parameter values, as well as the approach used to predict them from catchment information, incorporating interactions between parameters. This study proposes a generic framework to enable systematic regression regionalization for a data poor region, considering identification of model parameters using a multi-objective approach, and sensitivity analysis including consideration of parameter interactions. The approach developed has been applied to both lumped and distributed models, in order to investigate the benefits of adopting distributed models to represent catchment heterogeneity. The results indicate that a suitable regression approach can be developed for the region considered, outperforming directly calibrated parameters on a validation period, due to more accurate representation of the recharge process. However, no benefit was found for applying the approach on a distributed scale, most likely due to scale issues with the parameter values.