Application of data-driven algorithms for the forecasting of non-linear parameter

  IJRES-book-cover  International Journal of Recent Engineering Science (IJRES)          
© 2019 by IJRES Journal
Volume-6 Issue-2
Year of Publication : 2019
Authors : S.I. Abba, A.S. Maihula, M.B. Jibril, A.M. Sunusi, M.A. Ahmad, M. A. Saleh
DOI :    10.14445/23497157/IJRES-V6I2P103


MLA Style :S.I. Abba, A.S. Maihula, M.B. Jibril, A.M. Sunusi, M.A. Ahmad, M. A. Saleh "Application of data-driven algorithms for the forecasting of non-linear parameter" International Journal of Recent Engineering Science 6.2(2019):14-19. 

APA Style :S.I. Abba, A.S. Maihula, M.B. Jibril, A.M. Sunusi, M.A. Ahmad, M. A. Saleh, Application of data-driven algorithms for the forecasting of non-linear parameter. International Journal of Recent Engineering Science, 6(2),14-19.

Water quality index (W.Q.I.) is a widely used tool in different parts of the world to solve the problems of data management and to evaluate success and failures in management strategies for improving water quality. This study aimed to develop two Non-linear models (i.e., Adaptive neuro-fuzzy inference system (ANFIS) and Artificial neural network (ANN)) and a conventional linear model (viz: Autoregressive integrated moving average (ARIMA) models, in modelling the non-linear W.Q.I. at Kinta River, Malaysia and Yamuna River, India (Agra station). The performance of the models was assessed through Mean Square Error (M.S.E.), Root Mean Square Error (RMSE), Determination Coefficient (R2 ), and Mean Absolute Percentage Error (MAPE). The obtained result depicted the nonlinear models (ANF5S and ANN) can averagely increase the performance accuracy of linear models (ARIMA) up to 25% and 18% at Kinta and Yamuna River, respectively, in the verification phase. The overall results also demonstrated that the ANFIS model outperformed the other models, with the average increased up to 23% in the verification phase. Hence, serve as a suitable and reliable tool in forecasting the W.Q.I. in both of the regions.

[1] Sharma, D., & Kansal, A. (2011). Water quality analysis of River Yamuna using water quality index in the national capital territory , India ( 2000 – 2009 ), 0, 147–157.
[2] Muhammad, S. Y., Makhtar, M., Rozaimee, A., Aziz, A. A., & Jamal, A. A. (2015). Classification model for water quality using machine learning techniques. International Journal of Software Engineering and Its Applications, 9(6), 45-52.
[3] Abba, S. I., Said, Y. S., & Bashir, A. (2015). Assessment of Water Quality Changes at Two Location of Yamuna River Using the National Sanitation Foundation of Water Quality (NSFWQI). Journal of Civil Engineering and Environmental Technology, 2(8), 730-33.
[4] Singh, P. (2017). Review on Data Mining Techniques for Prediction of Water Quality, 8(5), 396–401.
[5] Nourani, V., Khanghah, T. R., Sayyadi, M., Prof, A., Student, M. S., & Student, B. S. (2013). Application of the Artificial Neural Network to monitor the quality of treated water, 3(1), 38–45.
[6] Gaya, M. S., Abdul Wahab, N., & Samsudin, S. I. (2014). ANFIS modeling of carbon and nitrogen removal in a domestic wastewater treatment plant. J. Teknol, 67(5), 29-34.
[7] Nourani, V., Elkiran, G., & Abba, S. I. (2018). Wastewater treatment plant performance analysis using artificial intelligence–an ensemble approach. Water Science and Technology.
[8] Singh, K. P., Basant, A., Malik, A., & Jain, G. (2009). Artificial neural network modeling of the river water quality — A case study, 220, 888–895.
[9] Gazzaz, N. M., Yusoff, M. K., Aris, A. Z., Juahir, H., & Ramli, M. F. (2012). Artificial neural network modeling of the water quality index for Kinta River (Malaysia) using water quality variables as predictors. Marine pollution bulletin, 64(11), 2409-2420.
[10] Khan, Y., & Chai, S. S. (2017). Ensemble of ANN and ANFIS for Water Quality Prediction and Analysis-A Data Driven Approach. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(2-9), 117-122.
[11] 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 Computer Science, 120, 75-82.
[12] Nourani, V., Hakimzadeh, H., & Amini, A. B. (2012). Implementation of artificial neural network technique in the simulation of dam breach hydrograph. Journal of HydroInformatics, 14(2), 478-496.
[13] K C Gouda, Libujashree R, Priyanka Kumari, Manisha Sharma, Ambili D Nair "An Approach for Rainfall Prediction using Soft Computing" International Journal of Engineering Trends and Technology 67.3 (2019): 158-164.
[14] Elkiran, G., Nourani, V., Abba, S. I., & Abdullahi, J. (2018). Artificial intelligence-based approaches for multi-station modelling of dissolve oxygen in river. Global Journal of Environmental Science and Management, 4(4), 439-450.
[15] Ki?i Ö, Nia AM, Gosheh MG, Tajabadi M.R.J., Ahmadi A (2012) Intermittent streamflow forecasting by using several data-driven techniques. Water Resour Manag 26:457–474.
[16] Legates, D. R., and McCabe, G. J. (1999). Evaluating the use of “goodness of fit” measures in hydrologic and hydroclimatic model validation. Water Resources Research, 35(1), 233-241.
[17] Emamgholizadeh, S., Kashi, H., Marofpoor, I., & Zalaghi, E. (2014). Prediction of water quality parameters of Karoon River (Iran) by artificial intelligence-based models. International Journal of Environmental Science and Technology, 11(3), 645-656.
[18] Sakizadeh, M. (2016). Artificial intelligence for the prediction of water quality index in groundwater systems. Modeling Earth Systems and Environment, 2(1), 8.
[19] Al Suhili, R. H., & Mohammed, Z. J. (2014). Comparison between Linear and Non-linear ANN Models for Predicting Water Quality Parameters at the Tigris River. Journal of Engineering, 20(10), 1-15.
[20] Hameed, M., Sharqi, S. S., Yaseen, Z. M., Afan, H. A., Hussain, A., & Elshafie, A. (2017). Application of artificial intelligence (A.I.) techniques in water quality index prediction: a case study in a tropical region, Malaysia. Neural Computing and Applications, 28(1), 893-905.
[21] Chen, W., & Liu, W. (2015). Water Quality Modeling in Reservoirs Using Multivariate Linear Regression and Two Neural Network Models, 2015.
[22] Kavasseri, R. G., and Seetharaman, K. (2009). Day-ahead wind speed forecasting using f-ARIMA models. Renewable Energy, 34(5), 1388-1393.

ANFIS, ANN, ARIMA, Kinta River, W.Q.I., Yamuna River.