A multi-model approach to analysis of environmental phenomena

Authors:
O. Giustolisi;A. Doglioni;D. A. Savic;B. W. Webb
Affiliations:
Civil and Environmental Engineering Department, Technical University of Bari, Engineering Faculty of Taranto, v.le del Turismo n.8, 74100 Taranto, Italy;Civil and Environmental Engineering Department, Technical University of Bari, Engineering Faculty of Taranto, v.le del Turismo n.8, 74100 Taranto, Italy;Centre for Water Systems, Department of Engineering, University of Exeter, School of Engineering, Computer Science and Mathematics, North Park Road, EX4 4QX Exeter, UK;Department of Geography, School of Geography, Archaeology & Earth Resources, University of Exeter, Rennes Drive, EX4 4RJ Exeter, UK
Venue:
Environmental Modelling & Software
Year:
2007

Citing 4
Cited 4

System identification: theory for the user

System identification: theory for the user
Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Symbolic and numerical regression: experiments and applications

Information Sciences—Informatics and Computer Science: An International Journal - Special issue on recent advances in soft computing

Using the Model Coupling Toolkit to couple earth system models

Environmental Modelling & Software
Software, Data and Modelling News: Computational evolutionary inverse lagrangian puff model

Environmental Modelling & Software
Scour depth modelling by a multi-objective evolutionary paradigm

Environmental Modelling & Software
Recent advances and future challenges for artificial neural systems in geotechnical engineering applications

Advances in Artificial Neural Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

A data-driven methodology named Evolutionary Polynomial Regression is introduced. EPR permits the symbolic and multi-purpose modelling of physical phenomena, through the simultaneous solution of a number of models. Multi-purpose modelling or ''multi-modelling'' enables the user to make a different choice according to what the model is aiming at: (a) the scientific knowledge based on data modelling, (b) on-line and off-line forecasting, (c) data augmentation (i.e. infilling of missing data in time series) and so on. This allows a more robust model selection phase. A case study based on the application of Evolutionary Polynomial Regression to the study of the thermal behaviour of a stream is presented.