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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Optimization of water quality index models using machine learning approaches.

Fei Ding1, Wenjie Zhang1, Shaohua Cao2

  • 1Key Laboratory of Beijing for Water Quality Science and Water Environment Recovery Engineering, College of Architecture and Civil Engineering, Beijing University of Technology, Beijing 100124, China.

Water Research
|July 20, 2023
PubMed
Summary

This study enhances water quality index (WQI) models using machine learning and game theory for improved accuracy. New aggregation functions reduce model uncertainty, offering better water quality assessment for river basins.

Keywords:
Chaobai River BasinCombined weightGame theoryLightGBMMachine learningWater quality assessment

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Area of Science:

  • Environmental Science
  • Water Resource Management
  • Data Science

Background:

  • Traditional water quality index (WQI) models face challenges in accuracy and uncertainty.
  • Optimizing parameter weights and aggregation functions is crucial for reliable water quality assessment.

Purpose of the Study:

  • To develop an optimized WQI assessment model by upgrading parameter weights and aggregation functions.
  • To enhance the accuracy and reduce the uncertainty of water quality models.
  • To establish a new water quality assessment system using the Chaobai River Basin as a case study.

Main Methods:

  • Determined combined weights using machine learning (LightGBM) and game theory (Analytic Hierarchy Process, Entropy Weight Method).
  • Proposed new aggregation functions: Sinusoidal Weighted Mean (SWM) and Log-weighted Quadratic Mean (LQM).
  • Developed and compared three WQI models (WQI_S, WQI_L, WQI_W) based on optimized weights and new functions.

Main Results:

  • The combined weight CW_AL, integrating AHP and LightGBM, was identified as optimal.
  • WQI_S and WQI_W models demonstrated low eclipsing problems (25.49% and 18.63%).
  • Model accuracy ranked WQI_S > WQI_W > WQI_L; WQI_S showed low uncertainty for poor water quality, WQI_W for good quality.

Conclusions:

  • The WQI_S model is recommended for assessing poor water quality, and WQI_W for good water quality.
  • Chaobai River Basin shows slight pollution, with better upstream water quality; TN is the main pollutant.
  • The developed model provides a reference for water quality assessment and scientific basis for regional water environment protection.