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A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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PM2.5 prediction based on modified whale optimization algorithm and support vector regression.

Zuhan Liu1,2, Xin Huang3, Xing Wang3

  • 1School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330099, China. lzh512@nit.edu.cn.

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|October 7, 2024
PubMed
Summary
This summary is machine-generated.

This study developed a hybrid model to predict atmospheric PM2.5 concentrations in Nanchang City. The modified Whale Optimization Algorithm-Support Vector Regression (mWOA-SVR) model achieved higher accuracy by incorporating both pollutant and weather data.

Keywords:
Correlation analysisPM2.5 concentrationSupport vector regressionWhale optimization calculation

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Area of Science:

  • Environmental Science
  • Atmospheric Chemistry
  • Data Science

Background:

  • Particulate Matter (PM2.5) poses significant air quality and health risks.
  • Accurate prediction of PM2.5 concentrations is crucial for environmental management.
  • Nanchang City faces challenges with atmospheric pollutant levels.

Purpose of the Study:

  • To develop a novel hybrid model for predicting PM2.5 concentrations in Nanchang.
  • To evaluate the effectiveness of incorporating meteorological factors into PM2.5 prediction.
  • To optimize the Support Vector Regression (SVR) model using a modified Whale Optimization Algorithm (WOA).

Main Methods:

  • Selected PM10, SO2, and CO as air pollutant features based on Pearson correlation coefficient (PCC).
  • Included daily maximum/minimum temperatures and wind power as meteorological features.
  • Employed a modified WOA (mWOA) for optimizing SVR model parameters, identifying four optimal combinations.

Main Results:

  • The hybrid mWOA-SVR model demonstrated enhanced prediction accuracy for PM2.5 concentrations.
  • Models incorporating both pollutant and meteorological data outperformed those using only pollutant data.
  • The optimized SVR model with selected features provided reliable PM2.5 forecasts.

Conclusions:

  • The mWOA-SVR model is effective for predicting PM2.5 variations in Nanchang.
  • Integrating meteorological factors significantly improves PM2.5 prediction accuracy.
  • This approach offers a valuable tool for air quality monitoring and mitigation strategies.