Identification of Wastewater Pollution through The Natural Language Generation with a Low-Cost Sensing Platform

Water pollution is a growing concern that affects ecosystems, human health, and industrial processes. Traditional water quality monitoring systems are often expensive and require expert analysis. This paper proposes a low-cost sensing platform integrated with Natural Language Generation (NLG) techniques to automatically detect and describe wastewater pollution. The system employs low-cost IoT sensors to measure key water quality parameters, including pH, turbidity, dissolved oxygen, and chemical contaminants. The collected sensor data is processed using machine learning models to identify pollution levels, and an NLG module translates the findings into human-readable reports. The proposed system provides an affordable, scalable, and real-time wastewater pollution detection solution, bridging the gap between raw sensor data and actionable insights for policymakers, industries, and environmental agencies.