This paper presents the implementation of an MS Excel tool for backpropagation neural networks in environmental engineering education. A number of test cases, Darcy friction factor and oxygen solubility, were also provided to test the performance of the tool. Relative mean square errors and coefficients of determination for Darcy friction factor were calculated as 1.53*10-3±0.59*10-3 and 0.9983±0.0003 while they were calculated as 4.78*10-4±2.33*10-4 and 0.9998±0.0000 for oxygen solubility. Results suggested that the tool’s performance is satisfactory. The tool produces satisfactorily fast and reliable results and can be used for environmental education in undergraduate/graduate level.
DEMİR, S., DEMİR, N. M., KARADENİZ, A., YÖRÜKLÜ, H. C. (2018). IMPLEMENTATION OF AN MS EXCEL TOOL FOR BACKPROPAGATION NEURAL NETWORK ALGORITHM IN ENVIRONMENTAL ENGINEERING EDUCATION. Sigma Mühendislik ve Fen Bilimleri Dergisi, Cilt: 36 - Sayı: 1, 251–260. https://ajindex.com/articles/419469
Henze M., Gujer W., Mino T., Matsuo T., Wentzel M.C., Marais G.v.R., (1999) Activated sludge model No. 2d, Water Sci. Technol. 39(1), 165-182
Ostace G.S., Critea V.M., Agachi P.Ş., (2011) Cost reduction of the wastewater treatment plant operation by MPC based on modified ASM1 with two-step nitrification/denitrification model, Comput. Chem. Eng. 35, 2469-2479
Demir S., Saral A., (2011) A new modification to the chemical mass balance receptor model for volatile organic compound source apportionment, Clean-Soil Air Water 39(10), 891-899
Demir S., Saral A., Ertürk F., Kuzu S.L., Goncaloğlu B.İ., Demir G., (2012) Effect of diurnal changes in VOC source strengths on performances of receptor models, Environ. Sci. Pollut. R. 19(5), 1503-1514
Demir S., Karadeniz A., Manav-Demir N., (2016) Artificial neural network simulation of cyclone pressure drop: Selection of the best activation function in Iraq, Pol. J. Environ. Stud. 25(5), 1891-1899
Şamlı R., Sivri N., Sevgen S., Kiremitci V.Z., (2014) Applying artificial neural networks for the estimation of chlorophyll-a concentrations along the Istanbul Coast, Pol. J. Environ. Stud. 23(4), 1281-1287
Elsayed K., Lacor C., (2012) Modeling and pareto optimization of gas cyclone separator performance using RBF type artificial neural networks and genetic algorithms, Powder Technol. 217, 84-99
Demir S., Karadeniz A., Manav-Demir N., (2016) Using steepness coefficient to improve artificial neural network performance for environmental modeling, Pol. J. Environ. Stud. 25(4), 1467-1477
Bayram A., Kankal M., (2015) Artificial neural network modeling of dissolved oxygen concentrations in a Turkish watershed, Pol. J. Environ. Stud. 24(4), 1507-1515
Jahandideh S., Jahandideh S., Asadabadi E.B., Askarian M., Movahedi M.M., Hosseini S., Jahandideh M., (2009) The use of artificial neural networks and multiple linear regression to predict rate of medical waste generation, Waste Manage. 29(11), 2874-2879
Aghav R.M., Kumar S., Mukherjee S.N., (2011) Artificial neural network modeling in competitive adsorption of phenol and resorcinol from water environment using some carbonaceous adsorbents, J. Hazard. Mater. 188, 67-77
Hernandez-Ramirez D.A., Herrera-Lopez E.J., Rivera A.L., del Real-Olvera J., (2014) Artificial neural network modeling of slaughterhouse wastewater removal of COD and TSS by electrocoagulation, Stud. Fuzziness Soft Comput. 312, 273-280