The GEP model, a recently-developed robust artificial intelligence technique, captures the benefits of both genetic algorithm and genetic programming by using chromosomes and expression trees. This paper presents a physical habitat simulation using the GEP model. The study area is a 2.5. km long reach of a stream, located downstream from a dam in the Dal River in Korea. Field monitoring revealed that Zacco platypus is the dominant species in the study area. The CCHE2D model and the GEP model were used for hydraulic and habitat simulations, respectively. Since the GEP model belongs to the data-driven approach, the model directly predicts the composite suitability index using the monitoring data. The GEP model is capable of considering correlations between all physical habitat variables, which is a clear advantage over knowledge-based models, such as the habitat suitability index model. The model was first validated using measured data. Distributions of the composite suitability index were then predicted using the GEP model for various flows. The predicted results were compared with those obtained using the habitat suitability index model. A sensitivity study of the GEP model was also carried out. Finally, the GEP model was used to construct habitat suitability curves for each physical habitat variable. The resulting habitat suitability curves were found to be very similar to those constructed by the method of Gosse (1982). The findings indicate that the conventional multiplicative aggregation method consistently underestimates the composite suitability index. Thus, the geometric mean method is proposed for use with calibrated coefficients.
Bibliographical notePublisher Copyright:
© 2015 Elsevier B.V.
All Science Journal Classification (ASJC) codes
- Environmental Engineering
- Nature and Landscape Conservation
- Management, Monitoring, Policy and Law