Gaoyou Lake IoT Pollution Risk — Water Quality Sensor Measurements [Environmental]

Overview

The Gaoyou Lake IoT Pollution Risk Dataset contains real environmental IoT sensor measurements and associated pollution risk indicators collected from Gaoyou Lake, a significant freshwater lake in Jiangsu Province, China. It captures a range of physicochemical water quality parameters measured by deployed IoT sensors, providing ground-truth data for developing ML models that classify pollution risk levels in real time.

Water quality monitoring using IoT sensor networks is a growing application domain driven by the need for continuous, low-cost environmental surveillance. IoT-based approaches using electrochemical sensors for pH, dissolved oxygen, turbidity, and conductivity enable the dense spatial and temporal sampling required for effective pollution risk assessment.

Structured to support binary or multi-class classification of pollution risk levels, with each sensor reading annotated with a risk category. Available on Kaggle and suited for comparative ML benchmarking on real environmental IoT data.

Column Schema

Key Statistics

• Source: IoT sensors at Gaoyou Lake, Jiangsu, China
• Parameters: pH, dissolved oxygen, turbidity, temperature, conductivity, ammonia nitrogen
• Label: Pollution risk classification (low / medium / high)
• File Format: CSV

Use Cases

• Aquatic pollution risk classification using real IoT environmental sensor data
• Real-time water quality monitoring for freshwater lake management
• Anomaly detection for sudden pollution events in IoT sensor networks
• Benchmarking ML classifiers for environmental IoT tasks

Source and Attribution

Publicly available on Kaggle. Reflects real IoT sensor measurements from Gaoyou Lake in the Yangtze River Delta region of China. Suited for environmental IoT and sustainable water management research.