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KSCE Journal of Civil Engineering

, Volume 22, Issue 9, pp 3233–3245 | Cite as

Comparison of ARIMA and NNAR Models for Forecasting Water Treatment Plant’s Influent Characteristics

  • Afshin Maleki
  • Simin Nasseri
  • Mehri Solaimany Aminabad
  • Mahdi Hadi
Environmental Engineering
First Online:
Received:
Accepted:

Abstract

A reliable forecasting model for each Water Treatment Plant (WTP) influent characteristics is useful for controlling the plant’s operation. In this paper Auto-Regressive Integrated Moving Average (ARIMA) and Neural Network Auto-Regressive (NNAR) modeling techniques were applied on a WTP’s influent water characteristics time series to make some models for short-term period (to seven days ahead) forecasting. The ARIMA and NNAR models both provided acceptable generalization capability with R2s ranged from 0.44 to 0.91 and 0.45 to 0.92, respectively, for chloride and temperature. Although a more prediction performance was observed for NNAR in comparison with ARIMA for all studied series, the forecasting performance of models was further examined using Time Series Cross-Validation (TSCV) and Diebold-Mariano test. The results showed ARIMA is more accurate than NNAR for forecasting the horizon-daily values for CO2, Cl and Ca time-series. Therefore, despite of the good predictive performance of NNAR, ARIMA may still stands as better alternative for forecasting task of aforementioned series. Thus, as a general rule, not only the predictive performance using R2 statistic but also the forecasting performance of a model using TSCV, are need to be examined and compared for selecting an appropriate forecasting model for WTP’s influent characteristics.

Keywords

time series analysis neural network auto-regressive model auto-regressive integrated moving average model water treatment plant forecasting 
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Copyright information

© Korean Society of Civil Engineers 2018

Authors and Affiliations

  • Afshin Maleki
    • 1
  • Simin Nasseri
    • 2
    • 3
  • Mehri Solaimany Aminabad
    • 1
  • Mahdi Hadi
    • 2
  1. 1.Dept. of Environmental Health Engineering, Environmental Health Research CenterKurdistan University of Medical SciencesSanandajIran
  2. 2.Center for Water Quality Research (CWQR), Institute for Environmental Research (IER)Tehran University of Medical SciencesTehranIran
  3. 3.Dept. of Environmental Health Engineering, School of Public HealthTehran University of Medical SciencesTehranIran

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