We propose a novel design methodology for the estimation of the quality of wastewater using Ultraviolet Visible (UV-Vis) spectroscopy and Machine Learning. Addressing the challenge posed by limited real-world data, particularly in highly polluted industrial environments, this study introduces a data augmentation method based on Conditional Generative Adversarial Networks (CGAN). The effectiveness of this method is evaluated by creating a regression model based on a Multi-layer Perceptron (MLP) to estimate the chemical oxygen demand, a water quality indicator, using the UV-Vis absorption spectrum. The proposed method demonstrates that insufficient wastewater sample data can be augmented to improve the performance of the regression task for chemical oxygen demand (COD) estimation.

Wastewater Quality Indicator Estimation Using Machine Learning and Data Augmentation Techniques

Cardia, Marco
;
Chessa, Stefano;Micheli, Alessio;Luminare, Antonella Giuliana;
2024-01-01

Abstract

We propose a novel design methodology for the estimation of the quality of wastewater using Ultraviolet Visible (UV-Vis) spectroscopy and Machine Learning. Addressing the challenge posed by limited real-world data, particularly in highly polluted industrial environments, this study introduces a data augmentation method based on Conditional Generative Adversarial Networks (CGAN). The effectiveness of this method is evaluated by creating a regression model based on a Multi-layer Perceptron (MLP) to estimate the chemical oxygen demand, a water quality indicator, using the UV-Vis absorption spectrum. The proposed method demonstrates that insufficient wastewater sample data can be augmented to improve the performance of the regression task for chemical oxygen demand (COD) estimation.
2024
9783031750120
9783031750137
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/1280129
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
social impact