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Open	Shravya Dasu is currently in her 3rd year studying Computer Science and Engineering at the RV College of Engineering, Bangalore, India. 2022 Winner of the Best Poster Research Award

Poster Abstract

Surface water and groundwater are the main sources of potable, safe-for-drinking water. Water that is fit for drinking is crucial for survival. Human activities frequently produce hazardous wastes and sewage, which contaminate the water and have a severe impact on the local flora and fauna. Consuming this contaminated water might lead to gastrointestinal issues as well as other illnesses. As a result, it is crucial to test the water and identify its potability.

Government data is freely available on an open platform run by the Indian government. Surface water quality data for the state of Karnataka from 1972 to 2021 is one such dataset. It includes information on the presence of several organic and inorganic compounds throughout time, including Aluminum, Fluorine, Iron, etc. The dataset also includes data on several other factors, including temperature, dissolved oxygen, BOD, total hardness, total dissolved particles, and total PH. The water is unfit for ingestion when certain chemicals are present in large amounts.

This project's major goal is to develop a visualization tool that will enable us to track the growth of various substances in water over time. By comparing the amount of these substances in the water sample with the WHO water potability requirements, the potability of water is determined. This will make it easier for the government to identify potential causes of water contamination and assist in the implementation of the necessary steps.

The two main tools used were the Machine Learning Library and the HPCC Systems Visualizer Bundle. The following are the key objectives accomplished:

• Visualization of various substances in water over time as bar graphs.

• Determination of the potability of water using the Learning Trees bundle.

• Calculation of BOD of the water and determining the goodness fit of the data using the Linear Regression bundle.

Bar graphs were used to visualize the presence of different compounds in the water in 1980 and 2021, which allows us to compare the growth of these substances over time. Using linear regression, the BOD was computed, and a goodness fit of 78% was found.

Presentation

In this Video Recording, Shravya provides a tour and explanation of her poster content.

Visualization and Determination of the Potability of the Surface Water

Click on the poster for a larger image.

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