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Determination of the Optimal Training Principle and Input Variables in Artificial Neural Network Model for the Biweekly Chlorophyll-a Prediction: A Case Study of the Yuqiao Reservoir, China
2015
PLoS ONE
Predicting the levels of chlorophyll-a (Chl-a) is a vital component of water quality management, which ensures that urban drinking water is safe from harmful algal blooms. This study developed a model to predict Chl-a levels in the Yuqiao Reservoir (Tianjin, China) biweekly using water quality and meteorological data from 1999-2012. First, six artificial neural networks (ANNs) and two non-ANN methods (principal component analysis and the support vector regression model) were compared to
doi:10.1371/journal.pone.0119082
pmid:25768650
pmcid:PMC4359150
fatcat:g5jwavo5lzf6vceidqtujgahzq