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

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  • Marijana Hadzima-Nyarko
  • Anamarija Rabi
  • Marija Šperac

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

Suggested Citation

  • Marijana Hadzima-Nyarko & Anamarija Rabi & Marija Šperac, 2014. "Implementation of Artificial Neural Networks in Modeling the Water-Air Temperature Relationship of the River Drava," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(5), pages 1379-1394, March.
    • Handle: RePEc:spr:waterr:v:28:y:2014:i:5:p:1379-1394
    DOI: 10.1007/s11269-014-0557-7
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    1. Yuan Yao & Zhenghua Gu & Yun Li & Hao Ding & Tinghui Wang, 2022. "Intelligent Simulation of Water Temperature Stratification in the Reservoir," IJERPH, MDPI, vol. 19(20), pages 1-13, October.
    2. Adam P. Piotrowski & Maciej J. Napiorkowski & Monika Kalinowska & Jaroslaw J. Napiorkowski & Marzena Osuch, 2016. "Are Evolutionary Algorithms Effective in Calibrating Different Artificial Neural Network Types for Streamwater Temperature Prediction?," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(3), pages 1217-1237, February.
    3. Bahaa Khali & Jan Adamowski, 2014. "Evaluation of the Performance of Eight Record-Extension Techniques Under Different Levels of Association, Presence of Outliers and Different Sizes of Concurrent Records: A Monte Carlo Study," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(14), pages 5139-5155, November.
    4. Gang Zhou & Manyi Cui & Junhong Wan & Shiqiang Zhang, 2021. "A Review on Snowmelt Models: Progress and Prospect," Sustainability, MDPI, vol. 13(20), pages 1-27, October.

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