Advances in data-driven analyses and modelling using EPR-MOGA
- 1 July 2009
- journal article
- conference paper
- Published by IWA Publishing in Journal of Hydroinformatics
- Vol. 11 (3-4), 225-236
- https://doi.org/10.2166/hydro.2009.017
Abstract
Evolutionary Polynomial Regression (EPR) is a recently developed hybrid regression method that combines the best features of conventional numerical regression techniques with the genetic programming/symbolic regression technique. The original version of EPR works with formulae based on true or pseudo-polynomial expressions using a single-objective genetic algorithm. Therefore, to obtain a set of formulae with a variable number of pseudo-polynomial coefficients, the sequential search is performed in the formulae space. This article presents an improved EPR strategy that uses a multi-objective genetic algorithm instead. We demonstrate that multi-objective approach is a more feasible instrument for data analysis and model selection. Moreover, we show that EPR can also allow for simple uncertainty analysis (since it returns polynomial structures that are linear with respect to the estimated coefficients). The methodology is tested and the results are reported in a case study relating groundwater level predictions to total monthly rainfall.Keywords
This publication has 5 references indexed in Scilit:
- An evolutionary multiobjective strategy for the effective management of groundwater resourcesWater Resources Research, 2008
- A multi-model approach to analysis of environmental phenomenaEnvironmental Modelling & Software, 2007
- A symbolic data-driven technique based on evolutionary polynomial regressionJournal of Hydroinformatics, 2006
- Optimal design of artificial neural networks by a multi-objective strategy: groundwater level predictionsHydrological Sciences Journal, 2006
- Using a multi-objective genetic algorithm for SVM constructionJournal of Hydroinformatics, 2006