Development of a precise regional-scale groundwater model by coupling MODFLOW & Machine Learning algorithms: A case study in Bist-Doab region, Punjab, India

Groundwater depletion in the Bist-Doab region of the Punjab State of India is a significant threat to sustainable agricultural practices, underscoring the need for effective management strategies. Modelling groundwater heads is essential for understanding groundwater flow dynamics, trends, and their interaction with surface water. It helps assess the aquifer's health, prevent over-extraction and contamination, and predict ambient groundwater responses to extreme events such as droughts or floods. Inaccurate groundwater models, which overestimate or underestimate groundwater levels and fail to capture temporal fluctuations, hinder proper water management. These errors lead to suboptimal decisions regarding water allocation and resource sustainability and ultimately impact crop yields and water availability.

This study aims to integrate physically-based models, such as those developed by MODFLOW, with machine-learning algorithms to improve prediction accuracy and support more informed decision-making. MODFLOW was used to simulate groundwater flow under both steady-state and transient conditions, utilizing field hydrogeological data from existing literature. Machine learning (ML) models, including Support Vector Regression (SVR), Random Forest (RF), and Artificial Neural Networks (ANN), were trained and tested on historical groundwater levels and meteorological data to enhance prediction accuracy.

The methodology employs data-driven models (DDMs) as error-correcting tools for the physically-based models. Historical residuals, calculated as the difference between observed and simulated groundwater heads, were used as inputs alongside features such as well location coordinates, simulated groundwater heads, and time of measurements. ML techniques such as SVR, RF and ANN were used to train the DDMs, which learn systematic errors in the physically-based model by analysing these historical residuals. Outputs include predicted systematic errors and updated groundwater heads, where corrections are applied to the initial simulated values. The effectiveness of the DDMs relies on the structure and patterns of the residuals in the physically-based model, with strong correlations between the groundwater heads, leading to better error correction and improved predictive accuracy.

Results show that integrating MODFLOW with ML, significantly reduces model error compared to traditional simulation approaches. The combined model effectively captures both seasonal fluctuations and long-term trends in groundwater levels, leading to more accurate predictions. The developed framework provides a reliable tool for improving groundwater resource management and optimizing water allocation strategies, ultimately supporting the sustainable management of groundwater in agriculturally stressed regions like Bist-Doab.