Tree‐based iterative input variable selection for hydrological modeling

By Stefano Galelli, A. Castelletti. 2013

128 citations recorded by the source corpus.

Subfields: Environmental Engineering, Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/a3381a4bcd6846f907fb92906f803404c12ba273

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In Environmental Engineering

In Global and Planetary Change

In Water Science and Technology

Citations in the corpus

Bootstrap rank‐ordered conditional mutual information (broCMI): A nonlinear input variable selection method for water resources modeling (2016, methodology): “…selection algorithms such as the Gamma Test [Stefansson et al., 1997; Wan Jaafar et al., 2011; Goyal et al., 2014], the Iterative Input Selection [Galelli and Castelletti, 2013], or the Genetic Programming [Tran et al., 2015] methods we do not consider these algorithms in this work to preserve…”
Implementation of a hybrid MLP-FFA model for water level prediction of Lake Egirdir, Turkey (2018, background): “resources management and lake-hydrology studies is: does a hybrid intelligent model integrated with an optimization algorithm boost the predictive performance? Attempting to seek answers to this question, a significant challenge faced by the hydrological system modelers is the proper selection of th”
Battling Arrow’s Paradox to Discover Robust Water Management Alternatives (2016, background): “Although there exist promising approaches for automatic selection and diagnosis of input variables (May et al. 2008a, b; Galelli and Castelletti 2013), there are several challenges for decision variable simplification in many objective analysis.”
Protocol for developing ANN models and its application to the assessment of the quality of the ANN model development process in drinking water quality modelling (2014, methodology): “…made: 1) Greater focus should be given to input independence in the input selection step to take into account input redundancy using methods such as partial mutual information (May et al., 2008; Fernando et al., 2009) or tree-based iterative input variable selection (Galelli and Castelletti, 2013).”
Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions (2014, methodology): “Alternatively, the use of techniques for the selection of inputs to data-driven models (Bowden et al., 2005a; Bowden et al., 2005b; Fernando et al., 2009; Galelli and Castelletti, 2013; Galelli et al., 2014; May et al., 2008a; May et al., 2008b) might be worthwhile.”