Abstract
Groundwater quality for irrigation purposes varies across regions, countries, and specific locations, primarily due to factors such as groundwater extraction and utilization methods, the intensity of rainfall, and the subsequent recharge of aquifers. The present study aimed to evaluate seasonal groundwater suitability for irrigation purposes using indexical approaches, statistical computing, graphical plotting, and machine learning algorithms. Groundwater samples were collected from intensively cultivated areas before and after the monsoon season and were analysed for different physicochemical parameters. The results revealed that the cation concentration of groundwater during pre-monsoon season followed the order of Na+ (59.9 %) > Ca2+(21.9%) > Mg2+ (17.1 %) > K+ (1.2%), whereas, anion chemistry followed the order of Cl−(50.9%) > HCO3− (43.6%) > SO42− (4.0 %) > CO32− (1.4%). A significant increase in the concentrations of major cations (Ca2+ and Na+) and anions (Cl− and HCO3−) was observed during the post-monsoon season. Various irrigation suitability indices expressed that 98.9, 73.5, 99.5, 56.12, and 97.9 % of groundwater samples were found suitable for irrigation based on electrical conductivity (EC), residual sodium carbonate (RSC), sodium adsorption ratio (SAR), percentage sodium (Na%) and water quality index (WQI), respectively. The factor and cluster analyses revealed that salinity, natural processes, and anthropogenic factors are the key determining factors affecting the correlations among water quality parameters. In the purview of predicting groundwater quality, multiple linear regression (MLR) and artificial neural network (ANN) models excelled, with ANN consistently outperforming MLR. Thus, it can be concluded that seasonal shifts in groundwater quality for irrigation are influenced by monsoon dynamics, demonstrating notable changes in cation and anion chemistry, with ANN models displaying superior predictive capabilities of irrigation suitability.
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