Implementation of Artificial Neural Networks in Modeling the Water-Air Temperature Relationship of the River Drava

Marijana Hadzima-Nyarko (), Anamarija Rabi and Marija Šperac

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2014, vol. 28, issue 5, 1379-1394

Abstract: Water temperature directly affects the physical, biological and chemical characteristics of the river and determines the fitness and life of all aquatic organisms. It has direct and indirect effects on nearly all aspects of stream ecology. Accurately estimating water temperature is a complex problem. The purpose of this article is to analyze the relationship between the air and water temperature of the River Drava by constructing an artificial neural network (ANN) model and choosing appropriate network architectures for the River Drava’s daily river water temperature as well as demonstrating its application in improving the interpretation of the results. A linear regression model, as well as a stochastic model are also constructed and compared to ANN models consisting of a multilayer perceptron neural network and a radial basis function network. The results indicate that the ANN models are much better models and that ANNs are powerful tools that can be used for the estimation of daily mean river temperature. Copyright Springer Science+Business Media Dordrecht 2014

Keywords: Mathematical modeling; Analysis of relationship between air and water temperature; Artificial neural networks (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:spr:waterr:v:28:y:2014:i:5:p:1379-1394

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DOI: 10.1007/s11269-014-0557-7

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