NIT Rourkela Develops ML-Based Model to Assess Groundwater Quality for Irrigation

Groundwater plays a crucial role in the agricultural sector, particularly in areas where surface water availability is limited. Researchers at the National Institute of Technology (NIT), Rourkela have recently developed a Machine Learning (ML) model to evaluate groundwater quality for irrigation. This groundbreaking research, conducted in Odisha’s Sundargarh district, has been published in the esteemed Water Quality Research Journal and holds immense potential for application across India.

Significance of Groundwater Quality in Agriculture

Agriculture is the backbone of Sundargarh’s economy, with paddy occupying 76% of the net cultivable area. The district’s surface water sources cover only 1.21% of the region, making groundwater an indispensable resource for irrigation. However, increasing agricultural demand, limited surface water availability, and population growth have led to rising groundwater extraction, adversely impacting both its quantity and quality. Poor-quality groundwater can severely affect crop yields and long-term soil fertility, necessitating a reliable method to assess and manage water quality effectively.

Research Methodology and Analysis

Data Collection and Examination

The research team, led by Dr. Anurag Sharma, Assistant Professor in the Civil Engineering Department at NIT Rourkela, analyzed groundwater samples collected from 360 wells across Sundargarh district. Advanced data analysis techniques were employed to assess key water quality parameters and their variations across different locations.

Chemical Properties Tested

The groundwater samples were tested for various chemical properties, including dissolved salts and essential minerals like sodium, calcium, and magnesium. These elements influence soil structure, permeability, and ultimately crop health.

Machine Learning and Statistical Tools Applied

The team utilized machine learning models and statistical tools to predict water quality trends and study changes over a seven-year period (2014-2021). Key indicators such as Sodium Adsorption Ratio (SAR), Kelly’s Ratio (KR), Percentage Sodium (Na), Permeability Index (PI), and Exchangeable Sodium Percentage (ESP) were examined to determine the suitability of groundwater for irrigation.

Key Findings and Insights

Regions with Good Groundwater Quality

The study found that groundwater in the southern, southwestern, and eastern parts of Sundargarh, including Rangaimunda, Lephripara, and Putudihi, is suitable for irrigation. These areas displayed stable groundwater quality with permissible levels of dissolved salts and minerals, ensuring optimal soil health and crop productivity.

Regions with Concerning Water Quality

In contrast, the western and central regions, particularly Krinjikela, Talsara, Kutra, and parts of Sundargarh town, showed higher concentrations of total dissolved solids and certain cations. Excessive sodium, calcium, and magnesium levels in these areas pose a threat to soil and crop health. If left unmanaged, these conditions could lead to declining yields, particularly for crops such as potato and cucumber.

Long-Term Trends and Implications

The study also identified consistent patterns of increase or decrease in key water quality indicators over time. This suggests that certain regions may face a further decline in groundwater suitability for irrigation in the future. The developed ML model provides a robust framework for predicting such changes and facilitating informed decision-making.

Future Applications and Benefits

Nationwide Implementation Potential

One of the most promising aspects of this research is its applicability across India. By utilizing this ML-based model, authorities can monitor and manage groundwater stress in different regions, ensuring sustainable irrigation practices.

Real-Time Water Quality Monitoring

The model has the potential to provide real-time insights into groundwater quality, allowing for timely interventions to protect irrigation-dependent farming communities. This can lead to better water resource management and enhanced agricultural productivity.

Policy and Decision-Making Support

With a data-driven approach, policymakers and stakeholders can make informed decisions regarding water conservation strategies and groundwater usage regulations. This can help in preventing excessive groundwater depletion and ensuring long-term agricultural sustainability.

Conclusion

The development of an ML-based model for groundwater quality assessment by NIT Rourkela marks a significant step toward sustainable water resource management in India. Given the increasing reliance on groundwater for irrigation, such innovative tools are essential for ensuring agricultural productivity and food security. With the potential for nationwide application, this research can revolutionize groundwater management practices, benefitting farmers and policymakers alike.

As India continues to face challenges related to water scarcity and agricultural sustainability, technological advancements like this ML model provide a ray of hope for a more secure and efficient future in water resource management.

NIT Rourkela, Machine Learning, Groundwater Quality, Irrigation, Agriculture, Water Management, Sustainable Farming, Groundwater Stress, Water Quality Assessment, India,

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