Abstract
Often in water quality management, understanding the relationship between input and output data might be a complicated process. In this situation Data Driven Models using information and collected data (input data) find out the relationship between inputs and outputs. In this regard, Artificial Neural Network (ANN) is one of the Data Driven Models which has recently been applied as a tool for modeling complicated processes. In this chapter, after reviewing the developing process of ANN in water quality management, the theory of the ANN is mentioned in detail for both static and dynamic methods. Data preparation, learning rate and model efficiency including selection of number of neurons in hiding layer which has a minimum error in learning rate and network efficiency is described in detail. At the end step, as a case study water quality of Zaribar Lake located in the Northwestern part of Iran, using Multilayer Perceptron (MLP) neural network method are described.
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Asadollahfardi, G. (2015). Artificial Neural Network. In: Water Quality Management. SpringerBriefs in Water Science and Technology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44725-3_5
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DOI: https://doi.org/10.1007/978-3-662-44725-3_5
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