Shuffled complex evolution approach for effective and efficient global minimization
Journal of Optimization Theory and Applications
Pixel-oriented database visualizations
ACM SIGMOD Record
Environmental Modelling & Software
Editorial: Methods of uncertainty treatment in environmental models
Environmental Modelling & Software
Sensitivity testing of a model for exploring water resources utilisation and management options
Environmental Modelling & Software
Environmental Modelling & Software
A fuzzy decision support tool for wildlife translocations into communal conservancies in Namibia
Environmental Modelling & Software
Hybrid fuzzy-mechanistic models for addressing parameter variability
Environmental Modelling & Software
Algebraic sensitivity analysis of environmental models
Environmental Modelling & Software
Identifiability analysis for receiving water body quality modelling
Environmental Modelling & Software
Environmental Modelling & Software
Environmental Modelling & Software
Short communication: Ten guidelines for effective data visualization in scientific publications
Environmental Modelling & Software
An open software environment for hydrological model assessment and development
Environmental Modelling & Software
Review: Three complementary methods for sensitivity analysis of a water quality model
Environmental Modelling & Software
Position paper: Characterising performance of environmental models
Environmental Modelling & Software
Environmental Modelling & Software
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The detailed evaluation of mathematical models and the consideration of uncertainty in the modeling of hydrological and environmental systems are of increasing importance, and are sometimes even demanded by decision makers. At the same time, the growing complexity of models to represent real-world systems makes it more and more difficult to understand model behavior, sensitivities and uncertainties. The Monte Carlo Analysis Toolbox (MCAT) is a Matlab library of visual and numerical analysis tools for the evaluation of hydrological and environmental models. Input to the MCAT is the result of a Monte Carlo or population evolution based sampling of the parameter space of the model structure under investigation. The MCAT can be used off-line, i.e. it does not have to be connected to the evaluated model, and can thus be used for any model for which an appropriate sampling can be performed. The MCAT contains tools for the evaluation of performance, identifiability, sensitivity, predictive uncertainty and also allows for the testing of hypotheses with respect to the model structure used. In addition to research applications, the MCAT can be used as a teaching tool in courses that include the use of mathematical models.