Image Dataset for Classification of Water Quality Based on Total Suspended Solids Using Convolutional Neural Networks

This dataset contains 6,518 labeled images of water samples classified into three pollution levels (low, medium, and high) based on total suspended solids (TSS) concentrations. The dataset was generated under controlled experimental conditions using image acquisition from video recordings of water samples prepared with varying concentrations of suspended solids. Images were extracted, filtered, and processed to ensure quality and consistency, forming a structured dataset suitable for machine learning and computer vision applications. The dataset supports the development and validation of artificial intelligence models, particularly convolutional neural networks, for automated water quality classification. This dataset is associated with the research article focused on the classification of water pollution using deep learning techniques and contributes to the advancement of low-cost, non-invasive monitoring methods for environmental systems.