Artificial Neural Network Models to Predict the Cost and Time of Wastewater Projects


  • Atheer M. Al-Saady College of Engineering - University of Baghdad
  • Sedqi E. Rezouki College of Engineering - University of Baghdad



Artificial neural network, wastewater projects, coefficient of correlation, mean absolute percentage error


Infrastructure, especially wastewater projects, plays an important role in the life of residential communities. Due to the increasing population growth, there is also a significant increase in residential and commercial facilities. This research aims to develop two models for predicting the cost and time of wastewater projects according to independent variables affecting them. These variables have been determined through a questionnaire distributed to 20 projects under construction in Al-Kut City/ Wasit Governorate/Iraq. The researcher used artificial neural network technology to develop the models. The results showed that the coefficient of correlation R between actual and predicted values were 99.4% and 99 %, MAPE was (26.24%), and (5.5%), and AA was (74%), and (94.5%), for cost and time model, respectively. The researcher concluded that the ANN model has a strong correlation and high accuracy, indicating that these models are characterized by high efficiency and good performance in predicting cost and time.


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How to Cite

Al-Saady , A. M. . and Rezouki , S. E. . (2023) “Artificial Neural Network Models to Predict the Cost and Time of Wastewater Projects”, Journal of Engineering, 29(1), pp. 93–109. doi: 10.31026/j.eng.2023.01.06.