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Water quality predictions through linear regression - A brute force algorithm approach.

A C P Fernandes1, A R Fonseca2, F A L Pacheco3

  • 1Centre for Natural Resources and Environment (CERENA/FEUP), Engineering Faculty, University of Porto, Rua Dr. Roberto Frias, Porto 4200-465, Portugal.

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Summary
This summary is machine-generated.

This study introduces a Python script for identifying optimal linear regression models, even with many variables. The tool automates the selection of significant regressors that meet statistical assumptions, streamlining model development.

Keywords:
Automatic selection of robust linear regression modelsContaminant emissionsGeographic information systemsLandscape metricsPython scriptWater quality

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Area of Science:

  • Environmental Science
  • Statistical Modeling
  • Data Science

Background:

  • Linear regression is a foundational statistical method, valuable for forecasting with limited data.
  • Selecting optimal regressors that satisfy all statistical assumptions can be complex when numerous predictors are available.

Purpose of the Study:

  • To develop an open-source Python script for automating the selection of best-fit linear regression models.
  • To efficiently test all regressor combinations using a brute-force approach.

Main Methods:

  • An open-source Python script was created to systematically evaluate all regressor combinations.
  • The script filters models based on user-defined thresholds for statistical significance, multicollinearity, error normality, and homoscedasticity.
  • The script also allows for the selection of models based on expected regression coefficients.

Main Results:

  • Less than 0.1% of millions of regressor combinations met the specified criteria for environmental data.
  • The script identified optimal linear regression models for predicting surface water quality parameters.
  • Geographically weighted regression yielded similar results, with performance varying by parameter (higher for pH and nitrate, lower for alkalinity and conductivity).

Conclusions:

  • The developed Python script effectively identifies optimal linear regression models by automating the testing of numerous regressor combinations.
  • The script aids researchers in selecting models that adhere to critical statistical assumptions and align with expectations.
  • The approach was successfully validated using an environmental dataset for water quality prediction.