Water Quality Prediction

Donated on 5/15/2022

Here we want to forecast the spatio-temporal water quality in terms of the “power of hydrogen (pH)” value for the next day based on the input data, which is the historical data of other water measurement indices. The input data consists of daily samples for 36 sites, providing measurements related to pH values in Georgia, USA. The input features consist of 11 common indices including volume of dissolved oxygen, temperature, and specific conductance (see details in dataset). The output to predict is the measurement of 'pH, water, unfiltered, field, standard units (Median)'. There are two major water systems to consider: one is centered on the city of Atlanta while the other is centered on the eastern coast of Georgia. This information indicates spatial dependency among different locations which are important to the forecast. For details of the data description, please refer to the file named README.docx. 'Specific conductance, water, unfiltered, microsiemens per centimeter at 25 degrees Celsius (Maximum)' 'pH, water, unfiltered, field, standard units (Maximum)' 'pH, water, unfiltered, field, standard units (Minimum)' 'Specific conductance, water, unfiltered, microsiemens per centimeter at 25 degrees Celsius (Minimum)' 'Specific conductance, water, unfiltered, microsiemens per centimeter at 25 degrees Celsius (Mean)' 'Dissolved oxygen, water, unfiltered, milligrams per liter (Maximum)' 'Dissolved oxygen, water, unfiltered, milligrams per liter (Mean)' 'Dissolved oxygen, water, unfiltered, milligrams per liter (Minimum)' 'Temperature, water, degrees Celsius (Mean)' 'Temperature, water, degrees Celsius (Minimum)' 'Temperature, water, degrees Celsius (Maximum)'

Dataset Characteristics

Other

Subject Area

Computer Science

Associated Tasks

Regression

Attribute Type

-

# Instances

705

# Attributes

-

Information

For what purpose was the dataset created?

The goal is to predict the spatio-temporal water quality in terms of the power of hydrogen (pH) value for the next day based on the historical data of water measurement indices.

Who funded the creation of the dataset?

National Science Foundation

What do the instances that comprise the dataset represent?

For each instance, The input features consist of 11 common indices including volume of dissolved oxygen, temperature, and specific conductance (see details in dataset). The output to predict is the measurement of 'pH, water, unfiltered, field, standard units (Median)'.

Has the dataset been used for any tasks already?

water quality prediction

Citation Requests/Acknowledgements

Liang Zhao, Olga Gkountouna, and Dieter Pfoser. 2019. Spatial Auto-regressive Dependency Interpretable Learning Based on Spatial Topological Constraints. ACM Trans. Spatial Algorithms Syst. 5, 3, Article 19 (August 2019), 28 pages. DOI: 10.1145/3339823

Download
1 citations
5415 views

Creators

Liang Zhao

liang.zhao@emory.edu

DOI

10.1145/3339823

License

This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.

This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.

By using the UCI Machine Learning Repository, you acknowledge and accept the cookies and privacy practices used by the UCI Machine Learning Repository.

Learn More