AI Article Synopsis
- This paper introduces a symbiotic adaptive neuro-evolutionary algorithm designed to create optimized neural network models specifically for hydrological modeling in the River Ouse catchment.
- It enhances traditional methods by evolving individual neurons and allows experimentation with various objective functions, not just the standard sum squared error, which recent studies indicate may not yield the best models.
- Results demonstrate that these evolved neural networks perform better than conventional models, particularly at longer lead times (6 and 24 hours), and highlight the limitations of using sum squared error as an objective function.
Article Abstract
This paper uses a symbiotic adaptive neuro-evolutionary algorithm to breed neural network models for the River Ouse catchment. It advances on traditional evolutionary approaches by evolving and optimising individual neurons. Furthermore, it is ideal for experimentation with alternative objective functions. Recent research suggests that sum squared error may not result in the most appropriate models from a hydrological perspective. Models are bred for lead times of 6 and 24 hours and compared with conventional neural network models trained using backpropagation. The algorithm is also modified to use different objective functions in the optimisation process: mean squared error, relative error and the Nash-Sutcliffe coefficient of efficiency. The results show that at longer lead times the evolved neural networks outperform the conventional ones in terms of overall performance. It is also shown that the sum squared error objective function does not result in the best performing model from a hydrological perspective.
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| http://dx.doi.org/10.1016/j.neunet.2006.01.009 | DOI Listing |
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