AI-Driven Groundwater Level Forecasting System Using Time Series Analysis and Neural Network Models

The primary aim of this project is to develop a machine learning model that accurately predicts future groundwater levels using time series data and environmental variables. Groundwater is a vital resource for drinking, agriculture, and industry, but overextraction and climate variability have led to its alarming depletion in many regions. This project addresses the urgent need for forecasting tools to inform sustainable water usage policies. The system will analyze historical data such as rainfall, extraction rates, temperature, and population density using regression and neural network models to anticipate groundwater trends. By the end of the project, students will produce a data-driven solution that helps communities and decision-makers plan resource management strategies more effectively.

The twelve-week project is designed to take students through the full lifecycle of an AI forecasting solution. Initially, students will familiarize themselves with core machine learning libraries (such as TensorFlow and Scikit-learn) and set up their environment using Python and tools like Anaconda Navigator or Google Colab. Data collection and preprocessing follow, including cleaning and formatting historical environmental datasets.

In the middle phase, students will experiment with time series modeling techniques, train regression and neural network models, and evaluate their performance on test datasets. Model tuning will focus on improving prediction accuracy. Later in the project, students will visualize results, summarize insights, and document their development process. Although complex geological data and global scalability are excluded from scope, students will focus on a specific region or country for practical demonstration. Final deliverables include a predictive model, user interface (if applicable), and a comprehensive technical presentation.