Harne Vaishnavi Sunil

GE4007

GROUNDWATER LEVELS PREDICTION

This project focuses on the prediction of groundwater levels for 2025 in the Purna River Sub-basin (Akola, Maharashtra) using XGBoost and Random Forest models, incorporating a climate scenario. It uses climate variables (temperature, precipitation, evapotranspiration) and groundwater levels (2015–2024) data. This study also identifies wells with significant negative or positive level changes and maps zones of deep versus shallow groundwater levels. Results reveal long-term trends, showing partial recovery but persistent stress areas. This work supports data-driven water management for sustainable resource planning amid climate challenges. 


Report Content

Introduction and Need for study

Study area , Aim and Objective

Methodology , Datasets , Wells locations

Climate Scenario

Groundwater Levels Prediction Using XGBoost Model

Groundwater Levels Prediction Using Random Forest Model

Model Comparison and Groundwater Levels Trend (2015-2025)

Well-Wise Groundwater Levels Change

Groundwater Depth Zonation Map

Conclusion , Recommendation , Limitations