Application of Geospatial and Machine Learning Algorithms for Groundwater Quality Prediction Used for Irrigation Purposes

Abstract

Abstract The main objective of the present study is to evaluate the groundwater quality for irrigation purposes in the central-western part of Haryana state (India). For this, 272 groundwater samples were collected during the Pre- and Post-monsoon periods in 2022. Several indices, including Sodium Absorption Ratio (SAR), Permeability Index (PI), Sodium Percentage (Na %), Kelly Ratio (KR), Magnesium Adsorption Ratio (MAR), and Irrigating water quality index (IWQI) were derived. The results in terms of SAR, Na%, and KR values indicate that the groundwater is generally suitable for irrigation. On the other hand, PI and MAR exceeded the established limits, primarily showing issues related to salinity and magnesium content in the groundwater. Furthermore, according to the groundwater quality assessment based on the IWQI classification, 47.06% and 25% of the total collected samples fell under the "Severe Restriction for irrigation" category during the Pre-monsoon and Post-monsoon periods, respectively. Spatial variation maps indicate that water quality in the western portion of the study area is unsuitable for irrigation during both periods. Three Machine learning (ML) algorithms, namely Random forest (RF), Support vector machine (SVM), and Extreme Gradient Boosting (XGBoost) were integrated and validated to predict the IWQI. The results revealed that the XGBoost with Random searchachieves the best prediction performances. The approaches established in this study have been confirmed to be cost-effective and feasible for groundwater quality, using hydrochemical parameters as input variables, and highly beneficial for water resource planning and management.