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Prediction of Irrigation Water Quality Indices Using Random Committee, Discretization Regression, REPTree, and Additive Regression

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Abstract

This study aims to evaluate the performance of four ensemble machine learning methods, i.e., Random Committee, Discretization Regression, Reduced Error Pruning Tree, and Additive Regression, to estimate water quality parameters of Biochemical Oxygen Demand BOD and Dissolved Oxygen DO. Data from Anbar City on the Euphrates River in western Iraq was employed for the model's training and validation. The best subset regression analysis and correlation analysis were used to determine the best input combinations and to ascertain variable correlation, respectively. Besides, sensitivity analysis was employed to determine the standardized coefficient for BOD and DO predictions, hence knowing the significance of the relevant physical and chemical parameters. Results revealed that temperature, turbidity, electrical conductivity, Ca++, and chemical oxygen demand were identified as the best input combinations for BOD prediction. In contrast, the variable combination of temperature, turbidity, chemical oxygen demand, SO4−1, and total suspended solids was identified as the best input combination for DO prediction. It was also demonstrated that the random committee model was superior for predictions of BOD and DO, followed by the discretization regression model. For predicting BOD (DO), the correlation coefficient and root mean square error were 0.8176 (0.7833) and 0.3291 (0.3544), respectively, during the testing stage. The present investigation provided approaches for addressing difficulties in irrigation water quality prediction through artificial intelligence techniques and thence serve as a tool to overcome the obstacles towards better water management.

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Data Availability

The data presented in this study are available at a reasonable request from the corresponding author.

Abbreviations

Temp:

Temperature

COD:

Chemical Oxygen Demand

Turb:

Turbidity

EC:

Electrical Conductivity

Ca:

Calcium

TSS:

Total Suspended Solids

SO4:

Sulphate

TDS:

Total Dissolved Solids

Alk:

Alkaline

REPTree:

Reduced Error Pruning Tree

RC:

Random Committee

AR:

Additive Regression

RD:

Regression by Discretization

CC:

Coefficient of Correlation

MAE:

Mean Absolute Error

RMSE:

Root Mean Square Error

RAE:

Root Absolute Error

RRSE:

Root Relative Standard Error

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Funding

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Authors and Affiliations

  1. Civil Engineering Department, University of Technology-Iraq, Baghdad, Iraq

    Mustafa Al-Mukhtar

  2. Department of Civil Engineering, Indian Institute of Technology (IIT) Kharagpur, Kharagpur, 721302, West Bengal, India

    Aman Srivastava

  3. Department of Civil Engineering, Indian Institute of Technology (IIT) Bombay, Mumbai, 400076, Maharashtra, India

    Leena Khadke

  4. Building and Construction Techniques Engineering Department, Al-Mustaqbal University College, 51001, Hilla, Babylon, Iraq

    Tariq Al-Musawi

  5. Agricultural Engineering Department, Faculty of Agriculture, Mansoura University, Mansoura, 35516, Egypt

    Ahmed Elbeltagi

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  1. Mustafa Al-Mukhtar
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  2. Aman Srivastava
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  3. Leena Khadke
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  4. Tariq Al-Musawi
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  5. Ahmed Elbeltagi
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Contributions

Ahmed Elbeltagi had the main idea of the paper; Mustafa Al-Mukhtar prepared the datasets; Ahmed Elbeltagi analyzed datasets by using a multi-collinearity statistical method, sensitivity method, best subset regression method, developed and implemented the ML models, supervision, Conceptualization, Funding Acquisition; Aman Srivastava and Leena Khadke: conducted analysis, developed plots, and drafted content for model description, results, and discussions; Tariq Al-Musawi writing, review and editing; Mustafa Al-Mukhtar completed abstract, introduction, study area description and conclusion; Aman Srivastava and Ahmed Elbeltagi improved and reviewed the manuscript sections. Mustafa Al-Mukhtar and Aman Srivastava have contributed equally to this work and shared the first authorship. All authors read and approved the final version of the paper.

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Correspondence to Ahmed Elbeltagi.

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Al-Mukhtar, M., Srivastava, A., Khadke, L. et al. Prediction of Irrigation Water Quality Indices Using Random Committee, Discretization Regression, REPTree, and Additive Regression. Water Resour Manage 38, 343–368 (2024). https://doi.org/10.1007/s11269-023-03674-y

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