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Comparative evaluation of classic and seasonal time series hybrid models in predicting electrical conductivity of Maroun river, Iran

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A Correction to this article was published on 27 May 2023

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Abstract

Seasonal autoregressive integrated moving average (SARIMA), Holt–Winters models (Hw), artificial multilayer perceptron neural network (ANN), seasonal time series hybrid models, Holt–Winter ANN (HN), and SARIMA–ANN hybrid models have been used to model and predict the parameter of monthly electrical conductivity (EC) of the Maroun river at Idenak hydrometer station. In this research, the data related to Khuzestan water and power authority organization has been used for 47 years from 1971 to 2018. Partial mutual information algorithm (PMI) was used to select the effective input parameter. The value of magnesium with a delay of 2 months and sodium with a delay of 1 month and the factors of temperature (with a delay of 1 month), acidity (with a delay of one month), and flow rate (with a delay of 2 months) were introduced as inputs to artificial neural networks in this study. By the values of the coefficient of determination 0.86 and the root mean square 11.3, SARIMA–ANN hybrid model has higher accuracy than the other models in predicting the monthly EC qualitative parameter. The results of this study showed that among the classical models, the neural network model with input parameters affected by the algorithm had better performance than the four classical models. Also, the weakest performance in predicting the quality parameter is the Holt–Winters model.

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Correspondence to Mehdi Panahi.

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Ahmadpour, A., Mirhashemi, S., Panahi, M. et al. Comparative evaluation of classic and seasonal time series hybrid models in predicting electrical conductivity of Maroun river, Iran. Sustain. Water Resour. Manag. 8, 165 (2022). https://doi.org/10.1007/s40899-022-00744-8

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  • DOI: https://doi.org/10.1007/s40899-022-00744-8

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