ES-NEAT calibrates about 20,000 parameters of a drinking water network model to an RMSE of 0.33 (validation 0.56) using expert-system bounds and evolved neural networks.
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Enhancing Accuracy and Efficiency in Calibration of Drinking Water Distribution Networks Through Evolutionary Artificial Neural Networks and Expert Systems
ES-NEAT calibrates about 20,000 parameters of a drinking water network model to an RMSE of 0.33 (validation 0.56) using expert-system bounds and evolved neural networks.