JKSWE

1

Abyaneh H. Z, 2014, Evaluation of multivariate linear regression and artificial neural networks in prediction of water quality parameters, Environmental Health Science and Engineering, Vol. 12, No. 1, pp. 1-8

2

Akratos C. S, Papaspyros J. N. E, Tsihrintzis V. A, 2008, An artificial neural network model and design equations for BOD and COD removal prediction in horizontal subsurface flow constructed wetlands, Chemical Engineering Journal, Vol. 143, No. 1-3, pp. 96-110

3

Bagheri M, Mirbagheri S. A, Ehteshami M, Bagheri Z, 2015, Modeling of a sequencing batch reactor treating municipal wastewater using multi-layer perceptron and radial basis function artificial neural networks, Process Safety and Environmental Protection, Vol. 93, pp. 111-123

4

Chalabi M and CH2M Beca, 2018, https://www.waternz.org.nz/Attachment?Action=Download&Attachment_id=2828 (accessed Nov. 2018), Artificial intelligence in the water industry: Myth or reality?

5

Chew C. M, Aroua M. K, Hussain M. A, 2018, Advanced process control for ultrafitration membrane water treatment system, Cleaner Production, Vol. 179, No. 2018, pp. 63-80

6

Djeddou M, Achour B, 2015, Wastewater treatment plant reliability prediction using artificial neural networks

7

Ding Y. R, Cai Y. J, Sun P. D, Chen B, 2014, The use of combined neural networks and genetic algorithms for prediction of river water quality, Journal of Applied Research and Technology, Vol. 12, No. 3, pp. 493-499

8

Elmolla E. S, Chaudhuri M, Eltoukhy M. M, 2010, The use of artificial neural network (ANN) for modeling of COD removal from antibiotic aqueous solution by the Fenton process, Journal of Hazardous Materials, Vol. 179, No. 1-3, pp. 127-134

9

Fernandez de Canete J, Saz-Orozco P. D, Baratti R, Mulas M, Ruano A, 2016, Soft-sensing estimation of plant effluent concentrations in a biological wastewater treatment plant using an optimal neural network, Expert Systems with Applications, Vol. 60, pp. 8-19

10

Giwa A, Daer S, Ahmed I, Marpu P. R, Hasan S. W, 2016, Experimental investigation and artificial neural networks ANNs modeling of electrically-enhanced membrane bioreactor for wastewater treatment, Water Process Enginering, Vol. 11, pp. 88-97

11

Griffiths K. A, Andrews R. C, 2011, Application of artificial neural networks for filtration optimization, Journal of Environmental Engineering, Vol. 137, No. 11, pp. 1040-1047

12

Güclü D, Dursun S, 2008, Amelioration of carbon removal prediction for an activated sludge process using an artificial neural network (ANN), Clean, Vol. 36, No. 9, pp. 781-787

13

Hamada M, Zaqoot H. A, Jreiban A. A, 2018, Application of artificial neural networks for the prediction of Gaza wastewater treatment plant performance-Gaza strip, Applied Research in Water and Wastewater, Vol. 5, No. 1, pp. 399-406

14

Kadri R. L, Boctor F. F, 2018, An efficient genetic algorithm to solve the resource-constrained project scheduling problem with transfer times: The single mode case, Operational Research, Vol. 264, No. 4, pp. 454-462

15

Khair U, Fahmi H, Hakim S. A, Rahim R, 2017, Forecasting error calculation with mean absolute deviation and mean absolute percentage error, Journal of Physics: Conference Series, Vol. 930, No. 1, pp. 1-6

16

Kim T. W, 2018, [Korean Literature], Case study for artificial intelligence application technology at sewage treatment plant, Master’s Thesis, Korea National University of Transportation

17

Kim Y. S, Lee J. H, Seo I. S, Kim H. D, Shin J. S, Na Y. Y, 2008, [Korean Literature], Application of artificial neural network with levenberg-marquardt algorithm in geotechnical engineering problem, Korean Geotechnical Society, pp. 987-997

18

Koo J. Y, 2018, [Korean Literature], Future water technology of The fourth industrial revolution period, Korean Society on Water Environment, pp. 11-12

19

Korea Environment Corporation (K-eco), 2016, https://www.hasudoinfo.or.kr/stat/statRefDetail.do (accessed Dec, 2018), Hasudo information system (hasudoinfo)

20

Korea Electric Power Corporation (KEPCO), 2016, http://home.kepco.co.kr/kepco/BD/BDBAPP002/BDBAPP002.do?menuCd=FN33020102 (accessed Dec. 2018), Electric Power Bigdata System

21

Kundu P, Debsarkar A, Mukherjee S, 2013, Artificial neural network modeling for biological removal of organic carbon and nitrogen from slaughterhouse wastewater in a sequencing batch reactor, Advances in Artificial Neural Systems, Vol. 2013, No. 13, pp. 1-15

22

Kwak Y. T, 2010, Accelerating levenberg-marquardt algorithm using variable damping parameter, [Korean Literature], The Korea Society of Computer and Information, Vol. 15, No. 4, pp. 57-63

23

Levenberg K, 1944, A method for the solution of certain non-linear problems in least squares, Quarterly of applied mathematics, Vol. 2, No. 2, pp. 164-168

24

Mamais D, Noutsopoulos C, Dimopoulou A, Stasinakis A, Lekkas T. D, 2014, Wastewater treatment process impact on energy savings and greenhouse gas emissions, Water Science & Technology, Vol. 71, No. 2, pp. 303-308

25

Man Y, Shen W, Chen X, Long Z, Corriou J. P, 2018, Dissolved oxygen control strategies for the industrial sequencing batch reactor of the wastewater treatment process in the papermaking industry, Environmental Science: Water Research & Technology, Vol. 4, No. 5, pp. 654-662

26

Manu D. S, Thalla A. K, 2017, Artificial intelligence models for prediction the performance of biological wastewater treatment plant in the removal of Kjeldahl Nitrogen from wastewater, Applied Water Science, Vol. 7, No. 7, pp. 3783-3791

27

Marquardt D. W, 1963, An algorithm for least-squares estimation of nonlinear parameters, The Society for Industrial and Applied Mathematics, Vol. 11, No. 2, pp. 431-441

28

McKenzie J, 2011, Mean absolute percentage error and bias in economic forecasting, Economics Letters, Vol. 113, No. 2011, pp. 259-262

29

Ministry of Environment (ME), 2009a, [Korean Literature], Development of application of the Korean artificial intelligent wastewater treatment model through the international joint research, Ministry of Environment

30

Ministry of Environment (ME), 2009b, http://www.me.go.kr/home/web/board/read.do?pagerOffset=0&maxPageItems=10&maxIndexPages=10&searchKey=title&searchValue=%EC%A0%81%EB%B0%9C&menuId=286&orgCd=&boardId=922550&boardMasterId=1&boardCategoryId=39&decorator= (accessed Dec. 2018), News & Archive

31

Ministry of Environment (ME), 2017, http://www.me.go.kr/gg/web/board/read.do?menuId=2290&boardMasterId=237&boardCategoryId=322&boardId=819120 (accessed Dec. 2018), News & Archive

32

Ministry of Environment (ME), 2018, http://www.me.go.kr/home/web/board/read.do?menuId=286&boardMasterId=1&boardCategoryId=39&boardId=167934 (accessed Dec. 2018), News & Archive

33

Myttenaere A, Golden B, Grand B. L, Rossi F, 2016, Mean absolute percentage error for regression models, Neurocomputing, Vol. 192, pp. 38-48

34

Nadiri A. A, Shokri S, Tsai F. T. C, Moghaddam A. A, 2018, Prediction of effluent quality parameters of a wastewater treatment plant using a supervised committee fuzzy logic model, Cleaner Production, Vol. 180, pp. 539-549

35

Nasr M. S, Moustafa M. A. E, Sief H. A. E, Kobrosy G. E, 2012, Application of artificial neural network (ANN) for the prediction of EL-AGAMY wastewater treatment plant performance-EGYPT, Alexandria Engineering, Vol. 51, No. 1, pp. 37-43

36

Panepinto D, Fiore S, Zappone M, Genon G, Meucci L, 2016, Evaluation of the energy efficiency of a large wastewater treatment plant in ltaly, Applied Energy, Vol. 161, No. 2016, pp. 404-411

37

Park J. E, Kim S. H, 2013, Application of levenberg marquardt method for calibration of unsteady friction model for a pipeline system, [Korean Literature], Journal of Korea Water Resources Association, Vol. 46, No. 4, pp. 389-400

38

Park S. H., Kim B. J., Kim O. H., Park J. T., 2008, Study on current energy consumption and recycling at public wastewater treatment plants in Korea, [Korean Literature], The Society of Air-Conditioning and Refrigerating Engineers of Korea, Vol. 37, No. 1, pp. 25-34

39

Picos A, Peralta-Hernández J. M, 2018, Genetic algorithm and artificial neural network model for prediction of discoloration dye from an electro-oxidation process in a press-type reactor, Water Science and Technology, Vol. 78, No. 4, pp. 925-935

40

Picos-Benítez A. R, López-Hincapié J. D, Chávez-Ramírez A. U, Rodríguez-García A, 2017, Artificial intelligence based model for optimization of COD removal efficiency of an up-flow anaerobic sludge blanket reactor in the saline wastewater treatment, Water Science and Technology, Vol. 75, No. 6, pp. 1351-1361

41

Sui Q, Jiang C, Yu D, Chen M, Zhang J, Wang Y, Wei Y, 2018, Performance of sequencing-batch membrane bioreactor(SMBR) with an automatic control strategy treating high-strength swine wastewater, Hazardous Materials, Vol. 342, No. 15, pp. 210-219

42

Vyas M, Modhera B, Vyas V, Sharma A. K, 2011, Performance forecasting of common effluent treatment plant parameters by artificial neural network, Engineering and Applied Sciences, Vol. 6, No. 1, pp. 38-42

43

Wan J, Huang M, Ma Y, Guo W, Wang Y, Zhang H, Li W, Sun X, 2011, Prediction of effluent quality of a paper mill wastewater treatment using an adaptive network-based fuzzy inference system, Applied Soft Computing, Vol. 11, pp. 3238-3246

44

Xin C, Addy M. M, Zhao J, Cheng Y, Ma Y, Liu S, Mu D, Liu Y, Chen P, Ruan R, 2018, Waste-to-biofuel integrated system and its comprehensive technoeconomic assessment in wastewater treatment plants, Bioresource Technology, Vol. 250, No. 2018, pp. 523-531

45

Xue H, 2017, Prediction of chemical oxygen demand emissions in wastewater treatment plant based on improved artificial neural network model, Chemical Engineering Transactions, Vol. 62, pp. 1453-1458

46

Yan P, Qin R, Guo J, Yu Q, Li Z, Chen Y, Shen Y, Fang F, 2017, Net-zero-energy model for sustainable wastewater treatment, Environmental Science & Technology, Vol. 51, No. 2, pp. 1017-1023

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Yoon J. M, Kim B. M, Lee S. H, 2018, Energy efficiency improvement technology of wastewater treatment facility applying IoT, The Korean Society for Energy, Special section I, Vol. 45

48

Zadorojniy A, Wasserkrug S, Zeltyn S, Lipets V, 2017, IBM cognitive technology helps aqualia to reduce costs and save resources in wastewater treatment, INTERFACES, Vol. 47, No. 5, pp. 411-424

JKSWE The Journal of
the Korean Society on Water Environment

Editorial Office

Journal XML

References

JKSWE The Journal ofthe Korean Society on Water Environment

Editorial Office

Journal XML

References

JKSWE The Journal of
the Korean Society on Water Environment