Forecasting influent-effluent wastewater treatment plant using time series analysis and artificial neural network techniques

Sameer Al-Asheh*, Farouq Sabri Mjalli, Hassan E. Alfadala

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)


We consider the problem of predicting the future behavior of wastewater treatment plant quality indicators by creating prediction models using historical plant data. One of the main aims of this work is to be able to predict plant operational situations in advance so that corrective actions can be taken in time. Sets of historical plant data, such as BOD, COD and TSS were collected for a local wastewater treatment plant in Doha, the capital of the State of Qatar. These variables characterize the performance of any wastewater treatment plant and can be considered as quality indicators of the plant performance. Data were collected over a period of 4 years for the influent and effluent streams of the station. The plant influent and effluent predictions were performed using different techniques. These include time-series analysis, where the ARIMA (Autoregressive Integrated Moving Average) model was implemented in this case, and two Artificial Neural Networks (ANN) algorithms, namely Adaptive Linear Neuron networks (ADALINE) and Multi-layer Feedforward (ML-FF) neural networks. The predictions from the three techniques were presented and compared. The ML-FF model predictions proved to be more reliable than that of the equivalent ARIMA predictions followed by the ADALINE predictions, particularly for the finial effluent stream variables.

Original languageEnglish
Article number3
JournalChemical Product and Process Modeling
Issue number3
Publication statusPublished - May 17 2007
Externally publishedYes


  • BOD
  • COD
  • Forecasting
  • MF-FF
  • Predictions
  • TSS
  • WWTP

ASJC Scopus subject areas

  • General Chemical Engineering
  • Modelling and Simulation


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