Data Science: Aquifer Level Predictions
Project Title: Aquifer Level Predictions at Water Technology Farm
Project: Click here to view.
Description: Underground water is vital to the irrigation of crops and plants, especially in the agricultural lands of Kansas. Many farms have sensors to detect water levels in aquifers to inform their agricultural practices. The School of Business and the Kansas Data Science Consortium jointly hosted the inaugural Data Science Competition at the University of Kansas, tasking participants to come up with detailed analyses and forecasts of the state's aquifer levels.
Objectives:
- To understand the trend of underground water in technological farms across Kansas.
- To identify key factors affecting the changes in underground water levels.
- To forecast levels of underground water in the years to come in reaction to climate change and human practices.
Result:
- Humidity, precipitation, and human practices are among the prominent factors influencing aquifer levels.
- The underground water reservoirs are decreasing in level over time.
- The models reasonably produced accurate forecasts of aquifer levels with the data provided.
- The project won first place at the 2023 Analytics and Information Systems Data Science Competition.
Tools:
- Data collection: Python
- Data storage: Microsoft Excel
- Data analysis and modeling: R
Project at a Glance:
