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Updated: May 31, 2026

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method

Published on: September 3, 2021

Wavelet transform-based artificial neural networks (WT-ANN) in PM10 pollution level estimation, based on circular

Maryam Shekarrizfard¹, A Karimi-Jashni, K Hadad

¹Department of Civil and Environmental Engineering, Shiraz University, Shiraz, Iran. maryam_shekarriz@yahoo.ca

Environmental Science and Pollution Research International

|July 8, 2011

Summary

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This study introduces a novel Wavelet Transform-Artificial Neural Network (WT-ANN) method for improved PM(10) estimation and prediction. The WT-ANN model demonstrates enhanced accuracy, speed, and robustness over traditional artificial neural network approaches.

Area of Science:

Environmental Science
Data Science
Signal Processing

Background:

Particulate Matter (PM(10)) pollution poses significant environmental and health risks.
Accurate estimation and prediction of PM(10) levels are crucial for effective environmental management.
Traditional methods often struggle with the complex, dynamic nature of air quality data.

Purpose of the Study:

To introduce a novel Wavelet Transform-Artificial Neural Network (WT-ANN) method for PM(10) estimation and prediction.
To leverage the multiresolution analysis and temporal shift properties of wavelet transforms for data preprocessing.
To investigate the relationship between PM(10) levels and circular meteorological variables using circular statistical indices.

Main Methods:

Application of wavelet transform for noise reduction and feature extraction from training data.

Related Experiment Videos

Last Updated: May 31, 2026

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method

Published on: September 3, 2021

Integration of wavelet transform with artificial neural networks (ANN) to create a WT-ANN model.

Utilization of circular statistical indices to analyze meteorological variables in relation to PM(10).

Main Results:

The WT-ANN model demonstrated significantly enhanced accuracy in PM(10) estimation and prediction.
The method showed improved speed of computation compared to traditional ANN models.
The WT-ANN approach exhibited a high degree of robustness in handling complex air quality data.

Conclusions:

Wavelet transform effectively reduces data perturbations, improving ANN model performance.
The developed WT-ANN model offers a superior approach for PM(10) forecasting.
This method provides a robust tool for environmental monitoring and air quality management.