AquaTrack is a web application designed to analyze and monitor water quality trends for a better, sustainable future. Built using Python and Django, AquaTrack provides insightful visualizations and data analysis to help users understand water quality across various water bodies and time periods.
AquaTrack is designed to educate the public on water health, inspire environmental solutions, and explore how external factors (such as climate change, pollution, and human activity) affect water quality over time. With a focus on user engagement, AquaTrack provides an easy-to-use platform for exploring over 20 years of water quality data.
AquaTrack offers a robust and user-friendly filter system that allows users to:
- Select specific water bodies for analysis.
- Customize a time range from 2002 to 2023.
- Analyze key water quality factors, such as temperature, pH, oxygen levels, conductivity, and turbidity.
The Anomalies Detected feature helps users spot unusual changes in water quality by providing:
- Location and date of anomalies.
- Water quality characteristics that may indicate potential issues.
AquaTrack uses the Surface Water Quality Monitoring Network dataset from open.canada.ca, covering over 20 years of reliable and detailed data on various water bodies across Canada. This dataset allows users to explore water quality trends and identify potential concerns in different locations.
Water quality is critical for:
- Public health: Ensuring access to clean drinking water.
- Ecosystems: Supporting aquatic life and biodiversity.
- Economies: Enabling agriculture, fisheries, and other industries dependent on water resources.
AquaTrack empowers users to understand these factors and helps inform decisions on water resource management. It provides valuable insights into water quality by:
- Helping users analyze and visualize water trends over time.
- Allowing exploration of the relationship between external factors and water health.
- Inspiring innovative solutions to improve water management.
In the future, AquaTrack aims to integrate machine learning to:
- Predict future water quality trends.
- Identify key monitoring factors.
- Recommend actionable solutions for improving water quality.
- Afif Ruddraw
- Muhammad Rafat Hossain
- Nafisah Nubah
- Zawad Atif