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