Application of data-driven algorithms for the forecasting of non-linear parameter
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.
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ANFIS, ANN, ARIMA, Kinta River, W.Q.I., Yamuna River.