Dynamically dimensioned search algorithm for computationally efficient watershed model calibration

By Bryan Tolson, Christine Annette Shoemaker. 2007

795 citations recorded by the source corpus.

Subfields: Environmental Engineering, Ocean Engineering, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/ca7fed61deacd767e07a43a49099a7eedf91e0ee

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In Environmental Engineering

In Ocean Engineering

In Water Science and Technology

Citations in the corpus

Ancient numerical daemons of conceptual hydrological modeling: 2. Impact of time stepping schemes on model analysis and prediction (2010, methodology): “The feasibility and competitiveness of Newton‐ type optimization for conceptual hydrological models is also indicated by recent successful applications of quasi‐Newton methods to simple versions of TOPMODEL and VIC [Kavetski et al., 2003b, 2006a] and to the Six Parameter (SIXPAR) model (but not the ”
Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions (2014, neutral): “…have been taken, including: binaries written in C/Cþþ or FORTRAN (e.g. Doherty, 2008; Duan et al., 1993; Hadka and Reed, 2013; Matott, 2005; Tolson and Shoemaker, 2007; Yan and Minsker, 2006, 2011); shared-object libraries with well-defined APIs (e.g. Keijzer et al., 2001; Levine et al.,…”
Model Performance Sensitivity to Objective Function during Automated Calibrations (2012, methodology): “For each calibration run, 3,000 SWAT simula tions were used for DDS.”
Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling (2009, methodology): “…Marce et al., 2008; Wang et al., 2009; Safari et al., 2009), because it conveniently enables the application of efficient single-criterion automated search algorithms , such as SCE (Shuffled Complex Evolution, Duan et al., 1992) or DDS (Dynamically Dimensioned Search, Tolson and Shoemaker, 2007).”
Numerical assessment of metamodelling strategies in computationally intensive optimization (2012, methodology): “…including: utilizing computationally efficient optimization algorithms (Doherty, 2005; Kuzmin et al., 2008; Tan et al., 2008; Tolson et al., 2009; Tolson and Shoemaker, 2007), including the efficient population-based evolutionary algorithms by (Hansen et al., 2003) and (Vrugt et al., 2009),…”
Automated configuration of algorithms for solving hard computational problems (2009, background): “…optimization (Stillger and Spiliopoulou, 1996), database server optimization (Diao et al., 2003), protein folding (Thachuk et al., 2007), formal verification (Hutter et al., 2007a), and even in areas far outside of computer science, such as water resource management (Tolson and Shoemaker, 2007).”
Appraisal of the generalized likelihood uncertainty estimation (GLUE) method (2008, methodology): “Mugunthan and Shoemaker [2006], Tolson and Shoemaker [2007] , Blasone et al. [2008a, 2008b] and others, have developed computationally efficient approaches for performing calibration and uncertainty analysis of complex environmental simulation models.”
Sensitivity-guided reduction of parametric dimensionality for multi-objective calibration of watershed models (2009, background): “In the last two decades, advances in global optimization have proven very useful for hydrological model identification [12,14,21,25,30,32,34,38,39,42,44].”