Cleaned Water Quality and Weather Dataset for AI-based Alum Prediction (2011–2024)

This dataset was developed to support research on predicting alum dosage in small water treatment plants. It combines daily plant records with weather data, including maximum temperature (TMAX). To make the data reliable for analysis and modeling, outliers and incorrect readings were carefully removed using logical and domain-based rules.

Records with clearly impossible or error values, such as extremely high or negative numbers, were deleted. Each variable was kept within realistic operating limits—for example, alum between 0 and 3500 mg/L, hardness between 5 and 1000 mg/L, and alkalinity between 2 and 1000 mg/L. Unusual readings like pH = 0.54 were also removed. Missing value rows were entirely removed from the dataset.

Through this cleaning process, the dataset became consistent, accurate, and ready for machine-learning models that can better predict chemical dosing and support safer, more efficient water treatment operations.