AI Enabled Water Well Predictor

Abstract

Groundwater has long served as a critical resource for agriculture, domestic use, and industry, particularly in regions lacking reliable surface water. However, the unpredictability of well yields has made sustainable extraction and planning increasingly difficult. Traditional well yield estimation methods, such as manual surveys and pump testing, are time-consuming, subjective, and lack scalability across diverse geological regions. This study introduces a machine learning-based water well yield predictor that leverages historical borewell data and site-specific features such as well depth, aquifer type, soil composition, and static water level. Using ensemble models, such as Random Forest, XGBoost, the system provides real-time predictions of both categorical yield levels and continuous flow rates. The models are trained with k-fold cross-validation and enhanced by correlation-based feature selection. Experimental results show 89% accuracy in classification and an R² of 0.93 for regression, significantly reducing the risk of low-yield drilling. The solution is deployable through a web interface and designed for continuous learning, supporting integration with GIS and climate data sources to aid long-term groundwater management.

Introduction

Groundwater is vital for human activities, especially in semi-arid and developing regions where it often serves as the primary water source. Traditional methods for estimating well yield, like geophysical surveys and pump tests, are labor-intensive, costly, and limited in scalability, leading to uncertainty and financial risks in well development.

Recent advances in artificial intelligence (AI) and machine learning (ML) offer scalable, accurate alternatives. This study proposes a machine learning framework using algorithms such as Random Forest, XGBoost, and Support Vector Machines (SVM) to predict well productivity based on features like well depth, aquifer type, static water level, soil composition, and land use. The model uses feature selection to focus on key variables, employs rigorous cross-validation, and continuously learns from new data for improved predictions.

The system supports both classification (categorizing wells by yield levels) and regression (predicting continuous flow rates), achieving high accuracy (89% classification accuracy with Random Forest) and strong regression performance (R² = 0.93 with XGBoost). It is deployed via a user-friendly web interface, providing quick yield estimates and confidence scores, helping planners reduce risks and promote sustainable groundwater management.

The modular design allows integration of future data sources like satellite imagery and real-time monitoring, ensuring adaptability and scalability. This intelligent, accessible tool enhances groundwater resource planning by reducing dependence on traditional field methods and improving decision-making efficiency.

Conclusion

This research presented a machine learning-based predictive framework for estimating water well yield, designed to address the constraints of traditional hydrogeological methods that are often resource-intensive, location-specific, and constrained by expert subjectivity. The recomended system integrates historical well data, geological parameters, and environmental attributes into an intelligent prediction model capable of estimating both categorical yield levels and continuous flow rates. Through the application of sophisticated algorithms like random forest and XGBoost, the framework demonstrated high results evaluated across different assessment measures, such as 89% classification accuracy and an R² of 0.93 in regression, thus significantly reducing uncertainty in well productivity estimation. The workflow was structured into clearly defined stages including data preprocessing, feature selection, model training, evaluation, and deployment. Each phase was designed to ensure reproducibility, scalability, and ease of understanding for both technical and non-technical users. The merging of derived characteristic like saturation indices and soil permeability enhanced the model\'s ability to capture complex subsurface interactions. Moreover, the system’s web-based user interface enabled real-time yield predictions supported by confidence scores and risk indicators, making it a practical tool for groundwater planners, engineers, and local authorities. These results validate the proposed framework as a reliable, data-driven alternative to exploratory drilling and manual yield estimation techniques. It aligns strongly with the original problem statement by providing a scalable, adaptive, and automated solution for groundwater resource planning. The framework minimizes financial and environmental risks, enhances decision-making accuracy, and bridges the gap between field-level operations and modern data analytics. Looking ahead, future modification in the system may include the assimilation of satellite imagery, IoT-based real-time monitoring sensors, and seasonal aquifer recharge data to further refine model accuracy. Additionally, incorporating time-series forecasting capabilities could allow the system to anticipate long-term yield variations due to climate change or land use transformations. These improvements would extend the framework’s applicability and resilience, further supporting sustainable groundwater management at regional and national levels.

References

[1] J. Chen, S. Kumar, and A. Srivastava, “Predicting Groundwater Levels Using Machine Learning,” Journal of Hydrology, vol. 562, no. 3, pp. 345–354, 2018. [2] R. Singh and V. Patel, “Water Table Dynamics from Remote Sensing Data,” Environmental Earth Sciences, vol. 78, no. 11, pp. 1–12, 2019. [3] L. Wang and S. Roy, “Random Forest for Aquifer Yield Classification,” Applied Water Science, vol. 10, no. 2, pp. 56–65, 2020. [4] P. Das and A. Gupta, “ML-Based Well Performance Forecasting,” Sustainable Water Resources Management, vol. 6, no. 4, pp. 55–64, 2020. [5] M. Lee and H. Kim, “Groundwater Mapping using SVM,” Computers and Geosciences, vol. 147, pp. 104642, 2021. [6] N. Sharma, T. Reddy, and A. Bajaj, “Feature Selection in Hydrogeological Modeling,” Water Resources Management, vol. 35, no. 3, pp. 945–958, 2021. [7] Y. Zhao and F. Lin, “XGBoost Models for Subsurface Water Flow,” Journal of Environmental Informatics, vol. 39, no. 2, pp. 123–131, 2022. [8] D. Verma and T. Joshi, “Geospatial Data Integration for Yield Prediction,” Geocarto International, vol. 37, no. 9, pp. 415–429, 2022. [9] K. Narayan and B. Rao, “Sustainable Groundwater Extraction Tools,” Groundwater for Sustainable Development, vol. 20, pp. 100765, 2023. [10] S. Mehta and R. Chauhan, “Risk Analysis for Well Drilling Sites,” Proceedings of the 2023 International Conference on Smart Water Systems, pp. 102–109, 2023.

Copyright

Copyright © 2025 Harshitha Arikeri, Mrs. Subhashree D C, Dr. Girish Kumar D. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.