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Predictive modeling the discharge of urban wastewater using artificial intelligent models (case study: Kerman city)

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Abstract

Urban wastewater discharge is one of the most important components for the development and design of water and wastewater treatment projects. In this study, the daily urban wastewater discharge (UWD) was predicted using two artificial intelligent models including the multilayered perceptron neural network (MLPNN) and genetic programming (GP). For this purpose, Kerman’s (Kerman city located in the south-east of Iran) sewage data, which have been recorded daily in the last 5 years, were used. To design the input pattern of the models, up to four-time delays units were considered. The results of both models declared that two delay units are enough for modeling and prediction of the UWD. The developed MLPNN model consists of two hidden layers. The first and second hidden layers have seven and five neurons, respectively. The tangent sigmoid was considered as transfer function governing the equation on neurons. The error indices of developed MLPNN in testing stages are R2 = 0.77 and RMSE = 1589. The structure of the mathematical model developed based on genetic programming has three genomes whose structure consists of two units of time delay. The accuracy of GP modeling is acceptable; however, its precision is a bit less than the MLPNN, but its results are more practical.

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Abbreviations

ANFIS:

Adaptive neuro-fuzzy inference system

BOD:

Biochemical oxygen demand

COD:

Chemical oxygen demand

DL:

Longitudinal dispersion coefficient

GP:

Genetic programming

GP:

Genetic programing

MLPNN:

Multilayered perceptron neural network

MLPNN:

Multilayered perceptron neural network

PCA:

Principal component analysis

TSS:

Total suspended solids

UWD:

Daily urban wastewater discharge

WWTP:

Wastewater treatment plants

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Authors and Affiliations

  1. Surface and Groundwater Resources Monitoring, Regional Water Company of Kerman, Kerman, Iran

    Ali Mansour-Bahmani

  2. Water Engineering Department, Lorestan University, Khorramabad, Iran

    Amir Hamzeh Haghiabi

  3. Department of Water and Soil Conservation, Ministry of Agriculture Jihad, Kerman, Iran

    Zahra Shamsi

  4. Hydro-Structure Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran

    Abbas Parsaie

Authors
  1. Ali Mansour-Bahmani
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  2. Amir Hamzeh Haghiabi
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  3. Zahra Shamsi
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  4. Abbas Parsaie
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Correspondence to Abbas Parsaie.

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Mansour-Bahmani, A., Haghiabi, A.H., Shamsi, Z. et al. Predictive modeling the discharge of urban wastewater using artificial intelligent models (case study: Kerman city). Model. Earth Syst. Environ. 7, 1917–1925 (2021). https://doi.org/10.1007/s40808-020-00900-z

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  • DOI: https://doi.org/10.1007/s40808-020-00900-z

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