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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Using Harris hawk optimization towards support vector regression to ozone prediction.

Robert Kurniawan1, I Nyoman Setiawan2, Rezzy Eko Caraka3,4

  • 1Department of Statistical Computing, Polytechnic Statistics STIS, 13330, DKI Jakarta, Indonesia.

Stochastic Environmental Research and Risk Assessment : Research Journal
|February 7, 2022
PubMed
Summary
This summary is machine-generated.

This study developed a predictive model for ozone concentration in JABODETABEK using Harris Hawks Optimization-Support Vector Regression (HHO-SVR). The HHO-SVR model demonstrated high accuracy and stability, particularly in Ciputat and South Bekasi sub-districts.

Keywords:
HHOJABODETABEKOzoneRFESVR

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Area of Science:

  • Environmental Science and Engineering
  • Atmospheric Chemistry and Air Quality Monitoring
  • Computational Intelligence and Machine Learning

Background:

  • JABODETABEK (Jakarta, Bogor, Depok, Tangerang, and Bekasi) faces significant air pollution, with ozone concentrations frequently exceeding healthy thresholds.
  • Effective pollution control and air quality restoration strategies require accurate ozone concentration prediction models.

Purpose of the Study:

  • To develop an advanced predictive model for ozone concentration in 14 JABODETABEK sub-districts.
  • To utilize the Harris Hawks Optimization-Support Vector Regression (HHO-SVR) algorithm for enhanced prediction accuracy.
  • To identify key meteorological and temporal factors influencing ozone levels.

Main Methods:

  • Collected ozone concentration data and meteorological factors (temperature, humidity, UV index) from online sources.
  • Employed Recursive Feature Elimination-Support Vector Regression (RFE-SVR) for significant predictor variable selection.
  • Developed the HHO-SVR model, optimizing Support Vector Regression (SVR) parameters using the Harris Hawks Optimization (HHO) algorithm.
  • Evaluated model performance using metrics like MAE, RMSE, MAPE, R², VR, and the Diebold-Mariano test.

Main Results:

  • Lag 1, lag 2, air temperature, humidity, and UV index were identified as significant predictors for most sub-districts via RFE-SVR.
  • The HHO-SVR model achieved excellent predictive performance in 7 out of 14 sub-districts, with MAE < 10, RMSE/MAPE < 20, R² ≈ 0.97, and VR ≈ 0.98.
  • The Diebold-Mariano test confirmed the superior accuracy and performance stability of the HHO-SVR model, especially in Ciputat and South Bekasi.

Conclusions:

  • The HHO-SVR model is a highly effective tool for predicting ozone concentrations in the JABODETABEK region.
  • Ciputat and South Bekasi sub-districts are particularly well-suited for implementing the HHO-SVR approach for air quality management.
  • The study highlights the potential of advanced optimization algorithms in addressing critical environmental challenges like air pollution.