ABSTRACT:

This resource contains a Python script used to clean and preprocess the alum dosage dataset from a small Oklahoma water treatment plant. The script handles missing values, removes outliers, merges historical water quality and weather data, and prepares the dataset for AI model training.

ABSTRACT:

This HydroShare collection contains three resources. The two datasets in the collection were used in the study ‘Artificial Intelligence to Assist Small Water Treatment Plant Operations.’ The first dataset (Raw Data) contains historical water treatment and Oklahoma Mesonet weather records from 2011–2024 in unprocessed form. The second dataset (Cleaned Data) is the processed and merged version of the same data, cleaned for duplicates, and missing values were removed. Together, they provide a transparent data pipeline from raw input to AI-ready dataset for modeling alum dosing.

ABSTRACT:

This dataset contains daily raw water treatment plant operational data and Oklahoma Mesonet weather data collected from 2011–2024. It includes inflow, pH, turbidity, alkalinity, alum dosage, and daily aggregated weather attributes such as TMAX, TMIN, humidity, and pressure. Data is provided in raw, pre-cleaning form for reproducibility.