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Water Quality Prediction Data Set
Download: Data Folder, Data Set Description

Abstract: 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.

Data Set Characteristics:  

Multivariate

Number of Instances:

705

Area:

Computer

Attribute Characteristics:

Real

Number of Attributes:

11

Date Donated

2020-11-17

Associated Tasks:

Regression

Missing Values?

N/A

Number of Web Hits:

21346


Source:

This dataset is arranged and partly derived from the United States Geological Survey.


Data Set Information:

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.


Attribute Information:

'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)'


Relevant Papers:

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:[Web Link]



Citation Request:

To use these datasets, please cite the paper:

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:[Web Link]


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