Detection of Waste Water Pollution Through Natural Language Generation

Abstract

Water pollution is a major environmental concern caused by industrialization, urbanization, agricultural activities, and improper disposal of domestic and industrial waste. Continuous monitoring of wastewater quality is essential for protecting human health, aquatic ecosystems, and water resources. This study proposes an intelligent wastewater pollution detection system that integrates Machine Learning and Natural Language Generation (NLG) for automated pollution analysis and report generation. The system analyzes important water-quality parameters, including pH, dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), total dissolved solids (TDS), turbidity, temperature, and heavy-metal concentration. The collected data is preprocessed by handling missing values, removing duplicate and noisy records, normalizing numerical features, and selecting relevant attributes. A Random Forest classifier is then employed to classify wastewater according to its pollution level. The classification results are subsequently processed by an NLG module to automatically generate human-readable environmental reports containing pollution status, major pollutants, environmental impacts, and recommended remedial measures. The proposed approach reduces manual analysis and reportgeneration effort while supporting faster and more consistent environmental decision-making. Keywords: Wastewater Pollution, Machine Learning, Random Forest, Natural Language Generation, Water Quality, Pollution Detection, Environmental Monitoring.