Depth-Integrated Estimation of Dissolved Oxygen in a Lake
Publication: Journal of Environmental Engineering
Volume 137, Issue 10
Abstract
The majority of variable estimation studies in water resources investigate the temporal variation of the variable. In this study, we examined the depth-dependent estimation of a lake’s dissolved oxygen (DO) using two artificial neural network (ANN) methods: (1) the radial basis functions (RBFs) and the feed forward back-propagation (FFBP), and (2) the multilinear regression (MLR). We tested two different input layer configurations. In the first case, we employed all other available lake parameters—total dissolved solids (TDS), pH, conductivity, lake depth, and lake temperature—to estimate DO. In the second case, we considered only depth and temperature to estimate DO. The performance evaluation criteria of these two cases were close. ANN estimation performances were noticeably superior to those of MLR, as reflected in the performance evaluation criteria and DO lake depth plots. We saw that the spatial variation of the lake’s DO can be captured by ANNs satisfactorily, even if available measurements are quite limited.
Get full access to this article
View all available purchase options and get full access to this article.
References
Brodnjak-Vonina, D., Dobnik, D., Novi, M., and Zupan, J. (2002). “Chemometrics characterisation of the quality of river water.” Anal. Chim. Acta, 462(1), 87–100
Broomhead, D., and Lowe, D. (1988). “Multivariable functional interpolation and adaptive networks.” Complex Syst., 2, 321–355
Cigizoglu, H. K. (2003). “Estimation, forecasting and extrapolation of flow data by artificial neural networks.” Hydrol. Sci. J., 48(3), 349–361
Cigizoglu, H. K. (2003). “Incorporation of ARMA models into flow forecasting by artificial neural networks.” Environmetrics, 14(4), 417–427
Cigizoglu, H. K. (2004). “Estimation and forecasting of daily suspended sediment data by multi layer perceptrons.” Adv. Water Resour., 27, 185–195
Cigizoglu, H. K. (2005). “Application of the generalized regression neural networks to intermittent flow forecasting and estimation.” J. Hydrol. Eng., 10(4), 336–341
Cigizoglu, H. K., and Kişi, O. (2006). “Flow prediction by three back propagation techniques using k-fold partitioning of neural network training data.” Nord. Hydrol., 36, 49–64
Deng, C., Wei, X., and Guo, L. (2006). “Application of neural network based on PSO algorithm in prediction model for dissolved oxygen in fishpond.” Proc., 6th World Congress on Intelligent Control and Automation, Dalian Fisheries Univ. Dalian, China
Eberhart, R. C., and Dobbins, R. W. (1990). “Neural network PC tools: A practical guide.” Academic, San Diego, 414
Hagan, M. T., and Menhaj, M. B. (1994). “Training feed forward techniques with the Marquardt algorithm.” IEEE Trans. Neural Networks, 5(6), 989–993
Partal, T., and Cigizoglu, H. K. (2009). “Prediction of daily precipitation using wavelet-neural networks.” Hydrol. Sci. J., 54(2), 234–246
Poggio, T., and Girosi, F. (1990). “Regularization algorithms for learning that are equivalent to multilayer networks.” Science, 247, 978–982
Schmid, B. H., and Koskiaho, J. (2006). “Artificial neural network modeling of dissolved oxygen in a wetland pond: The case of Hovi, Finland.” J. Hydrol. Eng., 11(2), 188–192
Şengörür, B., Doğan, E., Koklu, R., and Ve Samandar, A. (2006). “Dissolved oxygen estimation using artificial neural network for the water quality control.” Fresen. Environ. Bull., 15(9a), 1064–1067
Toprak, F., and Cigizoglu, H. K. (2008). “Predicting longitudinal dispersion coefficient in natural streams by artificial neural networks.” Hydrol. Processes, 22(20), 4106–4129
Information & Authors
Information
Published In
Copyright
© 2011 American Society of Civil Engineers.
History
Received: Jan 3, 2010
Accepted: Feb 11, 2011
Published online: Feb 14, 2011
Published in print: Oct 1, 2011
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.