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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27 May 2023
A Correction to this paper has been published: https://doi.org/10.1007/s40899-023-00863-w
References
Azad A, Karami H, Farzin S, Saeedian A, Kashi H, Sayyahi F (2017) Prediction of water quality parameters using ANFIS optimized by intelligence algorithms (case study: Gorganrood river). KSCE J Civ Eng 22(7):2206–2213
Barzegar R, Adamowski J, Moghaddam AA (2016) Application of wavelet-artificial intelligence hybrid models for water quality prediction: a case study in Aji-Chay river. Iran Stochastic Environ Res Risk Assess 30(7):1797–1819
Chowdhury M, Alouani A, Hossain F (2010) Comparison of ordinary kriging artificial neural network for spatial mapping of arsenic contamination of groundwater. Stochastic Environ Res Risk Assess 24(1):1–7. https://doi.org/10.1007/s00477-008-0296-5
Goyal MK, Ojha CSP (2011) Estimation of scour downstream of a ski-jump bucket using support vector and M5 model tree. Water Resour Manage 25(9):2177–2195
Guven A (2009) Linear genetic programming for time-series modeling of daily flow rate. J Earth Syst Sci 118(2):137–146
Hrdinka T, Novicky O, Hanslık E, Riede M (2012) Possible impacts of floods and droughts on water quality. J Hydro-Environ Res. https://doi.org/10.1016/j.jher.2012.01.008
Karimi S, Kisi O, Shiri J, Makarynskyy O (2013) Neuro-fuzzy and neural network techniques for forecasting sea level in Darwin Harbor, Australia. Comput Geosci 52:50–59. https://doi.org/10.1016/j.cageo.2012.09.015
Khuzestan water & power authority organization in Iran, 2018. annual reports
Kim M, Gilley JE (2008) Artificial neural network estimation of soil erosion and nutrient concentrations in runoff from land application areas. Comput Electron Agric 64(2):268–275. https://doi.org/10.1016/j.compag.2008.05.021
Kisi O (2006) Daily pan evaporation modeling using a neuro-fuzzy computing technique. J Hydrol 329:636–646
Kumar M, Raghuwanshi NS, Singh R, Wallender WW, Pruitt WO (2002) Estimating evapotranspiration using artificial neural networks. J Irrig Drain Eng ASCE 128(4):224–233. https://doi.org/10.1061/(ASCE)0733-9437(2002)128:4(224)
Nasr M, Zahran HF (2014) Using of pH as a tool to predict salinity of groundwater for irrigation purpose using artificial neural network. Egypt J Aquat Res 40(2):111–115. https://doi.org/10.1016/j.ejar.2014.06.005
Salami ES, Ehteshami M (2015) Simulation, evaluation and prediction modeling of river water quality properties (case study: Ireland Rivers). Int J Environ Sci Technol 12(10):3235–3242
Sanikhani H, Kisi O (2012) River flow estimation and forecasting by using two different adaptive neuro-fuzzy approaches. Water Resour Manage 26:1715–1729. https://doi.org/10.1007/s11269-012-9982-7
Tang L, Zeng G, Nourbakhsh F, Shen GL (2009) Artificial neural network approach for predicting cation exchange capacity in soil based on physico-chemical properties. Environ Eng Sci 26(1):137–146. https://doi.org/10.1089/ees.2007.0238
Tekin E, Akbas SO (2011) Artificial neural networks approach for estimating the groutability of granular soils with cement-based grouts. Bull Eng Geol Env 70(1):153–161. https://doi.org/10.1007/s10064-010-0295-x
Ye F, Zhang L, Zhang D, Fujita H, Gong Z (2016) A novel forecasting method based on multi-order fuzzy time series and technical analysis. Inf Sci 367:41–57. https://doi.org/10.1016/j.ins.2016.05.038
Yi X, Li G, Yin Y (2013) Comparison of three methods to develop pedotransfer functions for the saturated water content and field water capacity in permafrost region. Cold Reg Sci Technol 88:10–16. https://doi.org/10.1016/j.coldregions.2012.12.005
Yilmaz I, Kaynar O (2011) Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils. Expert Syst Appl 38(5):5958–5966. https://doi.org/10.1016/j.eswa.2010.11.027
Zorluer I, Icaga Y, Yurtcu S, Tosun H (2010) Application of a fuzzy rule-based method for the determination of clay dispersibility. Geoderma 160:189–196. https://doi.org/10.1016/j.geoderma.2010.09.017
Zou P, Yang J, Fu J, Liu G, Li D (2010) Artificial neural network and time series models for predicting soil salt and water content. Agric Water Manag 97:2009–2019. https://doi.org/10.1016/j.agwat.2010.02.011
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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