Document Type: ORIGINAL RESEARCH PAPER

Authors

1 Faculty of Civil and Environmental Engineering, Near East University, Near East Boulevard 99138, Nicosia, Cyprus

2 Department of Water Resources Engineering, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran

Abstract

ABSTRACT: In this study, adaptive neuro-fuzzy inference system, and feed forward neural network as two artificial intelligence-based models along with conventional multiple linear regression model were used to predict the multi-station modelling of dissolve oxygen concentration at the downstream of Mathura City in India. The data used are dissolved oxygen, pH, biological oxygen demand and water temperature at upper, middle and downstream of the river. To predict outlet of dissolved oxygen of the river in each station, considering different input combinations as i) 11 inputs parameters for all three locations except, dissolved oxygen at the downstream ii) 7 inputs for middle and downstream except dissolved oxygen, at the target location and lastly iii) 3 inputs for downstream location. To determine the accuracy of the model, root mean square error and determination coefficient were employed. The simulated results of dissolved oxygen at three stations indicated that, multi-linear regression is found not to be efficient for predicting dissolved oxygen. In addition, both artificial intelligence models were found to be more capable and satisfactory for the prediction. Adaptive neuro fuzzy inference system model demonstrated high prediction ability as compared to feed forward neural network model. The results indicated that adaptive neuro fuzzy inference system model has a slight increment in performance than feed forward neural network model in validation step. Adaptive neuro fuzzy inference system proved high improvement in efficiency performance over multi-linear regression modeling up to 18% in calibration phase and 27% in validation phase for the best models.

Graphical Abstract

Highlights

  • Artificial intelligence based modelling was carried out to predict the multi-station modelling of dissolved oxygen content in river
  • The result indicate that adaptive neuro fuzzy inference system model has slight increment in performance accuracy than feed forward neural network model
  • In comparison with the linear multi-linear regression model, artificial intelligence based models proved to be more reliable in prediction of dissolved oxygen content.

Keywords

Main Subjects

Abba, S.I.; Elkiran, G. (2017). Effluent prediction of chemical oxygen demand from the wastewater treatment plant using artificial neural network application. Procedia Comput. Sci., 120: 156-163 (8 pages).

Abba, S.I.; Hadi, S.J.; Abdullahi, J., (2017). River water modelling prediction using multi-linear regression, artificial neural network, and adaptive neuro-fuzzy inference system techniques. Procedia Comput. Sci., 120: 75-82 (8 pages).

Akrami, S.A.; Nourani, V.; Hakim, S.J.S., (2014). Development of nonlinear model based on wavelet-ANFIS for rainfall forecasting at Klang Gates Dam. Water Resour. Manage., 28(10): 2999–3018 (20 pages).

Areerachakul, S.; Junsawang, P.; Pomsathit, A., (2011). Prediction of dissolved oxygen using artificial neural network. International Conference on Computer Communication and Management, 5: 524–528 (5 pages).

ASCE Task Committee, (2000). Application of artificial neural networks in hydrology. I: Preliminary concepts. J. Hydrol. Eng., 5(2): 115-123 (9 pages).

Ay, M.; Kisi, O., (2011). Modeling of dissolved oxygen concentration using different neural network techniques in Foundation Creek, El Paso County, Colorado. J. Environ. Eng., 138(6): 654-662 (9 pages).

Chen, W.B.; Liu, W.C., (2014). Artificial neural network modeling of dissolved oxygen in reservoir. Environ. Monit. Assess., 186(2): 1203-1217 (15 pages).

Dogan, E.; Ates, A.; Yilmaz, E.C.; Eren, B., (2008). Application of artificial neural networks to estimate wastewater treatment plant inlet biochemical oxygen demand. Environ. Prog. Sustainable Energy, 27(4): 439-446 (8 pages).

Gaya, M.S.; Wahab, N.A.; Sam, Y.M.; Samsudin, S.I., (2014). ANFIS Modelling of Carbon and Nitrogen Removal in Domestic. J. Technol., 67(5): 29-34 (6 pages).

Hsu, K.L.; Gupta, H.V.; Sorooshian, S., (1995). Artificial neural network modeling of the rainfall‐runoff process. Water Resour. Res., 31(10): 2517-2530 (14 pages).

Jain, A.K.; Jain, K.A.; Punmia, B.C., (2014). Wastewater engineering (including air pollution). (B. Pumia, Ed.) (Second). India: Laxmi Publications (P) LTD 180-222 (43 pages).

Legates, D.R.; McCabe, G.J., (1999). Evaluating the use of “goodness‐of‐fit” measures in hydrologic and hydroclimatic model validation. Water Resour. Res., 35(1): 233-241 (9 pages).

Mamdani, E.H., (1974). Application of fuzzy algorithms for control of simple dynamic plant. In Proceedings of the Institution of Electrical Engineers., 121(12): 1585-1588 (4 pages).

Mirbagheri, S.A.; Nourani, V.; Rajaee, T.; Alikhani, A., (2010). Neuro-fuzzy models employing wavelet analysis for suspended sediment concentration prediction in rivers. Hydrol. Sci. J., 55(7): 1175–1189 (15 pages).

Najah, A.; El-Shafie, A.; Karim, O.A.; El-Shafie, A.H., (2014). Performance of ANFIS versus MLP-NN dissolved oxygen prediction models in water quality monitoring. Environ. Sci. Pollut., Res., 21(3): 1658-1670 (13 pages).

Nourani, V; Kisi, O.; Komasi, M., (2011). Two hybrid Artificial Intelligence approaches for modeling rainfall – runoff process. J. Hydrol., 402(1–2): 41–59 (19 pages).

Nourani, V.; Hakimzadeh, H.; Amini, A.B., (2012). Implementation of artificial neural network technique in the simulation of dam breach hydrograph. J. Hydroinfo., 14(2): 478–496 (19 pages).

Nourani, V.; Alami, M.T.; Vousoughi, F.D., (2015). Wavelet-entropy data pre-processing approach for ANN-based groundwater level modeling. J. Hydrol., 524: 255–269 (15 pages).

Nourani, V., (2017). An emotional ANN (EANN) approach to modeling rainfall-runoff process. J. Hydrol. 544: 267–277 (11 pages).

Olyaie, E.; Abyaneh, H.Z.; Mehr, A.D., (2017). A comparative analysis among computational intelligence techniques for dissolved oxygen prediction in Delaware River. Geosci. Front. 8(3): 517-527 (11 pages).

Parmar, K.S.; Bhardwaj, R., (2015). River water prediction modeling using neural networks, fuzzy and wavelet coupled model. Water Resour. Manage., 29(1): 17–33 (17 pages).

Quej, V.H.; Almorox, J.; Arnaldo, J.A.; Saito, L., (2017). ANFIS, SVM and ANN soft-computing techniques to estimate daily global solar radiation in a warm sub-humid environment. J. Atmos. Solar-Terrestrial Physics, 155: 62-70 (9 pages).

Ranković, V.; Radulović, J.; Radojević, I.; Ostojić, A.; Čomić, L., (2010). Neural network modeling of dissolved oxygen in the Gruža reservoir, Serbia. Ecol. Model., 221(8): 1239-1244 (6 pages).

Sharma, D.; Kansal, A., (2011). Water quality analysis of River Yamuna using water quality index in the national capital territory, India (2000–2009). Appl. Water Sci., 1(3-4): 147-157 (11 pages).

Singh, K.P.; Mohan, D.; Singh, V.K.; Malik, A., (2005). Studies on distribution and fractionation of heavy metals in Gomti River sediments—A tributary of the Ganges, India J. Hytrol., 312(1-4): 14-27 (14 pages).

Singh, K.P.; Basant, A.; Malik, A.; Jain, G., (2009). Artificial neural network modeling of the river    water quality-A case study. Ecol. Modell. 220: 888-895 (8 pages).

Solgi, A.; Zarei, H.; Nourani, V.; Bahmani, R., (2017). A new approach to flow simulation using hybrid models. Appl. Water Sci., 7(7): 3691-3706 (16 pages).

Takagi, T.; Sugeno, M., (1993). Fuzzy identification of systems and its applications to modeling and control. In readings fuzzy sets for intelligent systems, 387-403 (17 pages).

Turan, M.E.; Yurdusev, M.A., (2009). River flow estimation from upstream flow records by artificial intelligence methods. J.  Hydrol., 369(1-2): 71-77 (7 pages).

Verma, A.K.; Singh, T.N., (2013). Prediction of water quality from simple field parameters. Environ. Earth Sci., 69(3): 821-829 (9 pages).

Wen, X.; Fang, J.; Diao, M.; Zhang, C., (2013). Artificial neural network modeling of dissolved oxygen in the Heihe River, Northwestern China. Environ. Monit. Assess., 185(5): 4361-4371 (11 pages).

Yetilmezsoy, K.; Ozkaya, B.; Cakmakci, M., (2011). Artificial intelligence-based prediction models for environmental engineering. Neural Network World, 21(3): 193-218 (26 pages).

Zadeh, L.A. (1996). Fuzzy sets. In fuzzy sets, fuzzy logic and fuzzy systems: Selected papers by Lotfi A. Zadeh., 394-432 (39 pages).

 

HOW TO CITE THIS ARTICLE

Elkiran, G.; Nourani, V.; Abba, S.I.; Abdullahi, J., (2018). Artificial intelligence-based approaches for multi-station modelling of dissolve oxygen in river. Global J. Environ. Sci. Manage., 4(4): 439-450.


Letters to Editor


GJESM Journal welcomes letters to the editor for the post-publication discussions and corrections which allows debate post publication on its site, through the Letters to Editor. Letters pertaining to manuscript published in GJESM should be sent to the editorial office of GJESM within three months of either online publication or before printed publication, except for critiques of original research. Following points are to be considering before sending the letters (comments) to the editor.

[1] Letters that include statements of statistics, facts, research, or theories should include appropriate references, although more than three are discouraged.
[2] Letters that are personal attacks on an author rather than thoughtful criticism of the author’s ideas will not be considered for publication.
[3] Letters can be no more than 300 words in length.
[4] Letter writers should include a statement at the beginning of the letter stating that it is being submitted either for publication or not.
[5] Anonymous letters will not be considered.
[6] Letter writers must include their city and state of residence or work.
[7] Letters will be edited for clarity and length.

CAPTCHA Image