Abstract
Genetic Programming (GP) based modeling is suggested for modeling the variation of Dissolved Oxygen (DO) under controlled conditions in the presence and absence of toxicant. The results indicated that GP is able to evolve robust physically meaningful models even with small dataset by selecting the most relevant functions from the set of functions given for the modeling. It is interesting to note that the evolved models clearly reflect the underlying non-linearity of the process distinctly for both the case studies.
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Archana, S., Prashant, P.: River water quality modelling using artificial neural network technique. Aquat. Procedia 4, 1070–1077 (2015)
Antanasijevic, D., Pocajt, V., Povrenović, D., Perić-Grujić, A., Ristić, M.: Modelling of dissolved oxygen content using artificial neural networks: Danube River, North Serbia, case study. Environ. Sci. Pollut. Res. 20, 9006–9013 (2013)
Antanasijevic, D., Pocajt, V., Peric-Grujic, A., Ristic, M.: Modelling of dissolved oxygen in the Danube River using artificial neural networks and Monte Carlo Simulation uncertainty analysis. J. Hydrol. 519, 1895–1907 (2014)
Anyadike, C.C., Mbajiorgu, C.C., Ajah, G.N.: Prediction of the physico-chemical interactions of vimtim stream water quality using the aquatox model. IOSR J. Eng. 3(10), 1–6 (2013)
Areerachakul, S., Junsawang, P., Pomsathit, A.: Prediction of dissolved oxygen using artificial neural network. In: International Conference on Computer Communication and Management Proceedings of CSIT, vol. 5, Singapore, pp. 524–528 (2011)
Ay, M., Kisi, O.: Modeling of dissolved oxygen concentration using different neural network techniques in foundation Creek, El Paso County, Colorado. J. Environ. Eng. 138(6), 654–662 (2012)
Chen, W.B., Liu, W.C.: Artificial neural network modeling of dissolved oxygen in reservoir. Environ. Monit. Assess. 186(2), 1203–1217 (2014)
Deepshikha, S., Arun, K.: Assessment of river quality models: a review. Rev. Environ. Sci. Biotechnol. 12(3), 285–311 (2012)
Koelmans, A.A., Vander Heude, A., Knijff, L.M., Aalderink, R.H.: Integrated modelling of Eutrophication and organic contaminant fate and effects in aquatic ecosystems. a review. J. Water Resour. 35(15), 3517–3536 (2001)
Koza, J.R.: Genetic Programming: On the Programming of Computers by Natural Selection. MIT Press, Cambridge (1992)
Muttil, N., Chau, K.W.: Neural network and genetic programming for modelling coastal algal blooms. Int. J. Environ. Pollut. 28(3–4), 223–238 (2006)
Park, R.A., Clough, J.S., Wellman, M.C.: Aquatox modeling environmental fate and ecological effects in aquatic ecosystems. J. Environ. Model. 213, 1–15 (2008)
Radwan, M., Willems, P., El-sadek, A., Berlamont, J.: Modelling of dissolved oxygen and biochemical oxygen demand in river water using a detailed and a simplified model. Intl. J. River Basin Manage. 1(2), 97–103 (2003)
Schmid, B.H., Koskiaho, J.: Artificial neural network modeling of dissolved oxygen in a wetland pond: the case of Hovi, Finland. J. Hydrol. Eng. 11(2), 188–192 (2006)
Sivapragasam, C., Muttil, N., Jeselia, M.C., Visweshwaran, S.: Infilling of rainfall information using genetic programming. Aquat. Procedia 4, 1016–1022 (2015)
Sivapragasam, C., Mutti, N., Muthukumar, S., Arun, V.M.: Prediction of algal blooms using genetic programming. Mar. Pollut. Bull. 60, 1849–1855 (2010)
Wen, X., Fang, J., Diao, M., Zhang, C.: Artificial neural network modeling of dissolved oxygen in the Heihe River, Northwestern China. Environ. Monit. Assess. 185(5), 4361–4371 (2013)
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Vanitha, S., Sivapragasam, C., Nampoothiri, N.V.N. (2017). Modeling of Dissolved Oxygen Using Genetic Programming Approach. In: Arumugam, S., Bagga, J., Beineke, L., Panda, B. (eds) Theoretical Computer Science and Discrete Mathematics. ICTCSDM 2016. Lecture Notes in Computer Science(), vol 10398. Springer, Cham. https://doi.org/10.1007/978-3-319-64419-6_56
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