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Data on estimation for sodium absorption ratio: Using artificial neural network and multiple linear regressions.

Majid Radfard1, Hamed Soleimani2, Samira Nabavi2

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Data in Brief
|September 28, 2018
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Summary

Artificial neural networks (ANN) accurately predict groundwater sodium absorption ratio (SAR) in the Aras catchment area. ANN models outperformed multiple linear regression, offering a valuable tool for water quality assessment.

Keywords:
ArasGroundwater qualityMultiple linear regressionNeural networkSAR

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Area of Science:

  • Environmental Science
  • Hydrogeology
  • Water Quality Assessment

Background:

  • Groundwater quality is crucial for various uses, including irrigation.
  • Estimating the sodium absorption ratio (SAR) is vital for assessing groundwater suitability for agriculture.
  • The Aras catchment area's groundwater quality data from 2010-2014 requires robust analysis.

Purpose of the Study:

  • To estimate the sodium absorption ratio (SAR) of groundwater in the Aras catchment area.
  • To evaluate the effectiveness of Artificial Neural Network (ANN) models for SAR prediction.
  • To compare ANN performance against multiple linear regression (MLR) for groundwater quality assessment.

Main Methods:

  • Utilized a 3-layer MLP neural network with 4 input variables (pH, sulfate, chloride, EC) and 1 output (SAR).
  • Employed Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) for SAR estimation.
  • Evaluated model performance using Root Mean Square Error (RMSE), Mean Absolute Error (%), and correlation coefficient.

Main Results:

  • The impact of input variables on SAR prediction was quantified: EC (91%), chloride (94%), sulfate (72.22%), and pH (11.34%).
  • ANN demonstrated superior accuracy in predicting groundwater SAR compared to MLR.
  • ANN proved to be a precise and effective tool for estimating SAR in the Aras catchment groundwater.

Conclusions:

  • Artificial Neural Networks (ANN) are highly effective for predicting groundwater SAR.
  • ANN models offer significant advantages over traditional methods like MLR for water quality analysis.
  • The findings provide a reliable method for assessing groundwater suitability for agricultural purposes in the region.