| Idea Name: |
Water-Quality Analysis |
| Slogan: |
Water is life! |
| Supervisor Name: |
Dr. Rafia Mumtaz |
| Supervisor Designation: |
Assistant Professor|Head of Information Technology |
| Supervisor School: |
School of Electrical Engineering and Computer Sciences |
| Supervisor Department: |
Department of Computing |
| Contact number: |
05190852161 |
| Email ID: |
[email protected] |
| Abstract: |
| A time-series analysis and prediction of water quality will be performed using machine learning techniques. Water quality parameters will be obtained from Remote Sensing, Geoinformation System and IoT |
| What is the unmet need in society that your idea will fulfill ? |
| The increasing population, stressed resources, pollution, and the lack of technology-based solution for effective resource management raises the question of the quality of water available for near future generations. In this regard, the temporal analysis of water quality holds paramount importance. |
| Who needs it ? How many would benefit ? |
| The periodic time series analysis of water quality will help us in identifying key problems and finding effective solutions for improved quality in the near future for local citizens. The government should be alarmed in such a drastic situation of decreasing water quality. |
| How will the solution works |
| A Machine Learning model would be made based on water quality parameters’ (WQPs) data gathered from Geographical Information Systems, Remote Sensing, and IoT devices. WQPs like Turbidity, Dissolved Oxygen, Chlorophyll-a concentration will be used. After deriving the WQPs, a single-valued Water Quality Index will be calculated. Time-series analysis will be performed, and predictions will be made that would provide the government or other regulatory authorities with important insights. |
| Who are your competitors ? How is your solution different |
| Many researchers had the same idea, but we would be the first to apply it on such a varied dataset. With data being pooled from three different sources and being used, we aim to create a robust model that would give accurate results for other water bodies with similar conditions. |
| Status: |
new |
| Entry Date & Time: |
2020-12-24 (1844) |
