Application of RGB Color Analysis and Neural Networks for Preliminary Assessment of Wastewater Pollutant Concentration

This study presents a methodology for preliminary assessment of wastewater pollutant concentrations using RGB color intensity measurements combined with a multi-head neural network implemented in PyTorch. The model was trained on 120 samples from domestic, industrial, and synthetic sources to predict total pollutant concentration and identify the dominant RGB color. Key performance metrics include MAE = 0.012, RMSE = 0.018, MAPE = 2.1%, R2 = 0.96 for concentration prediction, and classification accuracy = 0.93 for dominant color identification. Observed color changes, primarily the rapid decrease of the red component, correlate with chemical and organic load, enabling visual differentiation of wastewater types. This approach provides a fast, low-cost, and equipment-free method for preliminary monitoring and has potential for real-time environmental applications. The proposed combination of RGB analysis with a multi-head neural network represents a novel approach for rapid, quantitative, and qualitative wastewater assessment.