An investigation on stream temperature analysis based on evolutionary computing

Research Area: Any Year: 2008
Type of Publication: Article Keywords: data-driven;evolutionary modelling;multiobjective optimization;thermal dynamics;on-line prediction;simulation
Journal: HYDROLOGICAL PROCESSES Volume: 22 (3)
Pages: 315-326
ISSN: 0885-6087
The data-driven technique, evolutionary polynomial regression, has been tested and used for the study of water temperature behaviour in the River Barle (south-west England). The study aimed to produce multiple models for forecasting water temperature, using air temperature as input. In addition, river discharge data were used to describe the hydrological regime of the study stream, even if they are not involved in the modelling phase. The availability of data sampled at hourly intervals allowed behaviour to be studied at several time scales, including short-term lags between air temperature and water temperature. The approach to model building differs from previous studies in that the relationship between air temperature and water temperature is not evaluated on the basis of a multi-parameter regression, nor does it identify particular structures; rather the evolutionary technique identifies the model by itself. In fact, the non-linear relationship between air temperature and water temperature is investigated by an evolutionary search in the space of particular pseudo-polynomials structures.
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