NIT Rourkela develops ML based model to assess groundwater quality for irrigation

NIT Rourkela: The research findings conducted in Odisha’s Sundargarh district have been published in the Water Quality Research Journal.

The study examined groundwater samples collected from 360 wells across Sundargarh. (Image: Wikimediacommons)
The study examined groundwater samples collected from 360 wells across Sundargarh. (Image: Wikimediacommons)

Press Trust of India | February 22, 2025 | 05:03 PM IST

NEW DELHI: Researchers at National Institute of Technology (NIT), Rourkela have developed a model based on Machine Learning to evaluate groundwater quality for irrigation purposes. The findings of the research, conducted in Odisha’s Sundargarh district, have been published in the prestigious Water Quality Research Journal.

According to officials, the developed model can be significantly used across the country to assess groundwater stress for irrigation purposes. Agriculture being central to the local economy and surface water sources covering only 1.21 pc of the district, groundwater is essential to meet the irrigation needs of this area.

Paddy, which occupies 76 pc of the net cultivable area, requires a large amount of water, making groundwater (GW) quality an essential factor for farmers, they said. Groundwater extraction in Sundargarh district has been increasing due to growing agricultural demand, limited surface water availability, and population growth. This has resulted in reductions in both the quantity and quality of groundwater. Poor-quality water can affect crop yields and long-term soil fertility.

According to Anurag Sharma, Assistant Professor, Civil Engineering Department, NIT Rourkela, advanced data analysis techniques were used to examine key water quality parameters and their variations across different parts of the district. The study examined groundwater samples collected from 360 wells across Sundargarh.

Statistical tools applied to predict water quality trends

These samples were tested for various chemical properties, including salts and minerals that can influence soil and crop health. Machine learning models and statistical tools were applied to predict water quality trends and understand how conditions have changed from 2014 to 2021.

"The findings indicate that groundwater in the southern, south-western and eastern parts of Sundargarh district, including areas around Rangaimunda, Lephripara and Putudihi is considered to be fit for irrigation. These regions showed stable groundwater quality with acceptable levels of dissolved salts and minerals along with the permissible range of Sodium Adsorption Ratio (SAR), Kelly’s Ratio (KR), Percentage Sodium (Na), Permeability Index (PI) and Exchangeable Sodium Percentage (ESP)," Sharma.

"However, the western and central parts of the district, particularly Krinjikela, Talsara and Kutra, and parts of Sundargarh town, have groundwater with comparatively higher concentrations of total dissolved solids and certain cations like sodium, calcium and magnesium, which may affect soil and crop productivity. If not managed properly, these conditions could lead to declining yields of potato and cucumber for this district,” Sharma said.

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Key findings of the study

One of the study's key findings is that important water quality indicators have been showing consistent patterns of increase or decrease over time. This suggests that some regions may experience a further decline in groundwater suitability. "The developed model can be significantly used on across the country to assess groundwater stress for irrigation purposes. By evaluating the groundwater quality, informed decisions can be taken by the authorities on degrading water resource management," he said. "Additionally, it can provide real-time insights on waiter quality, enabling productive interventions to safeguard irrigation-dependent farming communities across the country," Sharma said.

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