Are Evolutionary Algorithms Effective in Calibrating Different Artificial Neural Network Types for Streamwater Temperature Prediction ?
Adam T. Piotrowski , Maciej J. Napiórkowski , Monika Kalinowska , Jarosław J. Napiórkowski , Marzena Osuch
AbstractStreamwater temperature may be severely affected by the global warming. Different types of models could be used to evaluate the regime of water temperatures in future climatic conditions, including artificial neural networks. As neural networks have no physical background, they require calibration of large number of parameters. This is typically done by gradient-based algorithms, however there is an ongoing debate on usefulness of metaheuristics for this task. In this paper more than ten Swarm Intelligence and Evolutionary Algorithms, including one developed especially for this study, are tested to train four kinds of artificial neural networks (multi-layer perceptron, product-units, adaptive-network-based fuzzy inference systems and wavelet neural networks) for daily water temperature prediction in a natural river located in temperate climate zone. The results are compared with the ones obtained when the classical Levenberg-Marquardt algorithm is used. Finally, the ensemble aggregating approach is tested. Although the research confirms that most metaheuristics do not suite well for training any kind of neural networks, there are exceptions that include the newly proposed heuristic. However, the gain achieved when using even the best metaheuristics is low, comparing to the effort (computational time and complexity of such algorithms). Using ensemble aggregation approach seems to have higher impact on the model performance than seeking for new training methods
|Journal series||Water Resources Management, ISSN 0920-4741|
|Publication size in sheets||1|
|Keywords in English||Streamwater temperature prediction Temperate climate zone Artificial neural network Differential evolution Particle swarm optimization Genetic algorithm|
|Score|| = 40.0, 28-11-2017, ArticleFromJournal|
= 40.0, 28-11-2017, ArticleFromJournal
|Publication indicators||: 2016 = 2.848 (2) - 2016=3.214 (5)|
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