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Updated: Dec 6, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Application of M5 model tree optimized with Excel Solver Platform for water quality parameter estimation.

Maryam Bayatvarkeshi1, Monzur Alam Imteaz2, Ozgur Kisi3,4

  • 1Department of Soil Science, Malayer University, Malayer, Iran.

Environmental Science and Pollution Research International
|October 9, 2020
PubMed
Summary

Mathematical models improve water quality analysis. An M5 model tree, optimized with Excel Solver Platform (ESP), accurately estimated water quality parameters like total hardness (TH) and total dissolved solids (TDS).

Keywords:
Catchment monitoringExcel Solver Platform (ESP)M5 mode treeWater quality

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Area of Science:

  • Environmental Science
  • Water Resource Management
  • Data Mining

Background:

  • Determining water quality parameters is costly and time-consuming.
  • Mathematical models can reveal connections between water quality indicators.
  • Data mining offers efficient techniques for water quality assessment.

Purpose of the Study:

  • To present and improve a data mining technique for estimating water quality parameters.
  • To analyze surface and groundwater quality data from Hamedan, Iran (2006-2015).
  • To compare the performance of the M5 model tree and its Excel Solver Platform (ESP) optimized version.

Main Methods:

  • Utilized M5 model tree and a modified version optimized with Excel Solver Platform (ESP).
  • Analyzed water quality data including electrical conductivity (EC), total dissolved solids (TDS), sodium adsorption ratio (SAR), and total hardness (TH) as target variables.
  • Used pH, sodium (Na), chlorine (Cl), bicarbonate (HCO3), sulfate (SO4), magnesium (Mg), calcium (Ca), and potassium (K) concentrations as input parameters.

Main Results:

  • pH was the least influential parameter for EC, TDS, SAR, and TH in both groundwater and surface water.
  • Models showed higher accuracy in estimating total hardness (TH) compared to other parameters; SAR proved complex.
  • The M5-ESP model significantly reduced the normal root mean error (NRMSE) by 18.95% for groundwater and 20.29% for surface water compared to the M5 model.

Conclusions:

  • The Excel Solver Platform (ESP) optimization enhances the accuracy of the M5 model tree for water quality parameter estimation.
  • Groundwater quality parameter prediction using both M5 and M5-ESP models outperformed surface water prediction.
  • Data mining techniques, particularly the M5-ESP model, offer a cost-effective and efficient approach to water quality assessment.