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Water Quality Algal Bloom Dataset
Data Science and Analytics
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About
This collection of data focuses on the evaluation of algae concentration in river systems, addressing the ecological challenge posed by high concentrations of harmful algae. The primary purpose is to enable the development of predictive models that can accurately forecast algal frequencies based on chemical properties. Monitoring algal blooms is crucial for improving river quality and protecting life forms. Utilizing chemical monitoring, which is inexpensive and easily automated, allows researchers to potentially bypass the slow and costly biological analysis that typically requires skilled labour.
Columns
The data file contains 19 attributes collected for water samples:
- Index: A unique identifier for the water sample record.
- season: The time of year the observation was made (e.g., winter, spring).
- size: The recorded size of the river (e.g., small, medium).
- speed: The speed at which the water is flowing (e.g., high, medium).
- mxPH: The maximum measured pH value.
- mnO2: The minimum value of dissolved oxygen ($O_2$).
- Cl: The average value of chloride ($Cl$).
- NO3: The average value of nitrates ($NO^−_3$).
- NH4: The average value of ammonium ($NH^+_4$).
- oPO4: The average value of orthophosphate ($PO_4^{3-}$).
- PO4: The average value of total phosphate.
- Chla: The average value of chlorophyll.
- a1 - a7: The frequency of occurrence (concentration) for each of the seven identified harmful algae species (algae number 1 through algae number 7).
Distribution
This dataset is packaged as a CSV file named
algae.csv, approximately 19.28 kB in size. It contains 19 columns and 200 records, all of which are valid for analysis. The column types include categorical features (like season, size, and speed) and numerical measurements for the various chemical and biological concentrations.Usage
This data is ideally applied for developing machine learning or statistical models designed to predict harmful algal bloom frequencies. Specifically, it supports the creation of models that use measured chemical parameters to forecast the presence and concentration of seven specific algae species. This facilitates the implementation of inexpensive and automated systems for environmental monitoring.
Coverage
The samples were gathered from different European rivers over a period spanning approximately one year. The data incorporates temporal factors (season) and physical environmental factors (river size and water speed) alongside detailed chemical property measurements and the resulting biological concentrations of the harmful algae species.
License
CC0: Public Domain
Who Can Use It
The dataset is intended for environmental scientists, hydrologists, data scientists, and public health researchers. Users focused on predictive modelling can utilize the chemical parameters to forecast biological outcomes. Governmental or municipal agencies involved in water quality management can use these predictive insights to improve monitoring efficiency and enact early warnings regarding ecological issues.
Dataset Name Suggestions
- River Algae Chemical Predictor
- Harmful Algae Concentration Evaluation
- Water Quality Algal Bloom Dataset
- European River Algae Frequency Data
- Chemical-Biological River Monitoring
Attributes
Original Data Source: Water Quality Algal Bloom Dataset
QUALITY