Time Series Analysis, Regression, and Neural Networks
Groundwater is a critical resource for agriculture, industry, and household use. However, depleting groundwater levels have become a major global concern, especially in areas prone to drought or excessive water usage. Accurately predicting future groundwater levels can help policymakers, farmers, and industries make informed decisions to ensure sustainable water usage.
This project involves building a machine learning model to predict future groundwater levels using historical data such as rainfall patterns, water extraction rates, temperature, soil type, and population growth. The model will use time series forecasting techniques to anticipate fluctuations in groundwater levels and provide actionable insights for water resource management.
Basic Knowledge of Programming Languages like Python, R, TensorFlow, Scikit-learn etc.
Before Commencing the project the following links have to be examined.
https://www.kaggle.com/
https://hydrolearn.org/
https://www.researchgate.net/
https://github.com/
