Application of Gene Expression Programming to water dissolved oxygen concentration prediction

Akilandeswari, S.; Adline Mahiba, H., (2013). Prediction of BOD values in engineering work industrial effluent by Anfis modeling. Int. J. Res. Pure Appl. Phys., 3(2): 7–9 (3 pages).

Alte, P. D.; Sadgir, P. A., (2015). Water quality prediction by using ANN. Int. J. Adv. Found. Res. In Sci. Eng., 1: 278-285 (8 pages).

Altinbilek, D..; Usul, N.; Yazicioglu, H.; Kutoglu, Y.; Merzi, N.; Gögüs, M.; Doyuran, V. ; Günyakti, A., (1995). Water resources and environment management plan project for Gölvesi-Mogan-Ayrir Lake. In Mogan and Eymir lakes first environment Conference, 13–21 (9 pages). (In Turkish)

Azamathulla, H. M.; Ghani, A. A.; Leow, C. S.; Chang, C. K.; Zakaria, N. A., (2011). Gene-expression programming for the development of a stage-discharge curve of the Pahang River. Water Resour. Manage., 25(11): 2901-2916 (16 pages).

Beklioglu, M.; Ince, O.; Tuzun, I., (2003). Restoration of the eutrophic Lake Eymir, Turkey, by biomanipulation after a major external nutrient control I. Hydrobiologia, 490(1): 93-105 (13 pages).

Chu, H.B.; Lu, W.X.; Zhang, L., (2013). Application of artificial neural network in environmental water quality assessment. J. Agric. Sci. Technol. 15(2): 343–356 (14 pages)

Ding, Y.R.; Cai, Y.J.; Sun, P.D.; Chen, B., (2014). The use of combined neural network and genetic algorithms for prediction of water quality. J. Appl. Res. Technol.,  12(3) : 493–499 (7 pages)

Ferreira, C., (2006). Gene expression programming: mathematical modeling by an artificial intelligence, Vol. 21. Berlin-Heidelberg, Springer-Verlag.

Ferreira, C., (2002). Gene expression programming in problem solving. Part VI, In Soft computing and industry, Springer, London, 635-653 (19 pages).

Ferreira, C., (2001). Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst., 13(2): 87–129 (43 pages).

Jain, A.; Kumar, A. M., (2007). Hybrid neural network models for hydrologic time series forecasting. Appl. Soft Comput., 7(2): 585-592 (8 pages).

Karimi, S.; Shiri, J.; Kisi, O.; Shiri, A.A., (2016). Short-term and long-term streamflow prediction by using'wavelet–gene expression's programming approach. J. Hydraul. Eng., 22(2): 148-162 (15 pages).

Kisi, O.; Akbari, N.; Sanatipour, M.; Hashemi, A.; Teimourzadeh, K.; Shiri, J., (2013). Modeling of Dissolved Oxygen in River Water Using Artificial Intelligence Techniques. J. Environ. Inf., 22 (2): 92-101 (10 pages).

 Kişi, Ö., (2008). River flow forecasting and estimation using different artificial neural network techniques. Hydrol. Res., 39(1): 27-40 (14 pages).

Koza, J.R., (1992). Genetic programming: on the programming of computers by means of natural selection, Vol. 1, MIT Press, Cambridge.

Lacombe, G.; Douangsavanh, S.; Vogel, R. M.; McCartney, M.; Chemin, Y.; Rebelo, L. M.; Sotoukee, T., (2014). Multivariate power-law models for streamflow prediction in the Mekong Basin. J. Hydrol.: Reg. Stud., 2: 35-48 (14 pages).

Lihua, C.; Shengquan, M.; Li, L., (2008). A model to evaluate do of river based on artificial neural network and stylebook. J. Hainan Normal Univ. (Nat. Sci.), 21(4) : 372–376 (5 pages).

Liu, S.; Tai, H.; Ding, Q.; Li, D.; Xu, L.; Wei, Y., (2013). A hybrid approach of support vector regression with genetic algorithm optimization for aquaculture water quality prediction. Math. Comput. Modell., 58(3): 458-465 (8 pages).

Martí, P.; Shiri, J.; Duran-Ros, M.; Arbat, G.; De Cartagena, F.R.; Puig-Bargués, J., (2013). Artificial neural networks vs. gene expression programming for estimating outlet dissolved oxygen in micro-irrigation sand filters fed with effluents. Comput. Electron. Agric., 99: 176-185 (10 pages).

McKnight, U. S.; Funder, S. G.; Rasmussen, J. J.; Finkel, M.; Binning, P. J.; Bjerg, P. L., (2010). An integrated model for assessing the risk of TCE groundwater contamination to human receptors and surface water ecosystems. Ecol. Eng., 36(9): 1126-1137 (12 pages).

Mitchell, M., (1996). An Introduction to Genetic Algorithms, MIT Press Cambridge, MA, USA.

 Palani, S.; Liong, S.Y.; Tkalich, P.; Palanichamy, J., (2009). Development of a neural network model for dissolved oxygen in seawater. Indian J. Mar. Sci., 38(2): 151-159 (9 pages).

Sarkar, A.; Pandey, P., (2015). River water quality modeling using artificial neural network technique. Aquatic Procedia, 4: 1070-1077 (8 pages).

 

 

 Pour, S. H.; Harun, S. B.; Shahid, S., (2014). Genetic programming for the downscaling of extreme rainfall events on the East Coast of Peninsular Malaysia. Atmos., 5(4): 914-936 (23 pages).

Rounds, S. A., (2002). Development of a neural network model for dissolved oxygen in the Tualatin river, Oregon, In Second Federal Interagency hydrologic modeling conference, Las Vegas, Nevada, 1–13 (13 pages).

Salami, E. S.; Ehteshami, M., (2015). Simulation, evaluation and prediction modeling of river water quality properties (case study: Ireland Rivers). Int. J. Environ. Sci. Technol., 12(10): 3235–3242 (8 Pages).

Razaq, S. A.; Shahid, S.; Ismail, T.; Chung, E. S.; Mohsenipour, M.; Wang, X. J., (2016). Prediction of flow duration curve in ungauged catchments using genetic expression programming. Procedia Eng., 154: 1431-1438 (8 pages).

Scholten, H.; Kassahun, A.; Refsgaard, J. C.; Kargas, T.; Gavardinas, C.; Beulens, A. J. M., (2007). A methodology to support multidisciplinary model-based water management. Environ. Modell. Software. 22(5) : 743–759 (17 pages).

Simeonov, V.; Einax, J.; Stanimirova, I.; Kraft, J., (2002). Environmetric modeling and interpretation of river water monitoring data. Anal. Bioanal.Chem, 374(5): 898-905 (8 pages).

Singh, K. P.; Basant, N.; Gupta, S., (2011). Support vector machines in water quality management. Anal. Chim. Acta, 703(2): 152-162 (11 pages).

Tayfur, G., (2002). Artificial neural networks for sheet sediment transport. Hydrol. Sci. J., 47(6): 879-892 (14 pages).

Yagbasan, O.; Yazicigil, H., (2009). Sustainable management of Mogan and Eymir Lakes in central Turkey. Environ. Geol., 56(6): 1029-1040 (12 pages).