Nitrate contamination prediction in Groundwater data in Karnataka, India, using Machine Learning (ML) Techniques

Groundwater is a natural water source crucial in sustaining ecosystems and meeting various human needs. Groundwater is often contaminated due to the various anthropogenic and non-anthropogenic activities. Nitrate is the most abundant pollutant of Groundwater, which may be exogenic and anthropogenic. We studied nitrate ion concentration in Groundwater from dug well data. The Karnataka state's nitrate ion concentration varies from 0 to 1696 mg/l, which is higher in most places than the admissible limit of 45 mg/l as per the World Health Organisation (WHO). The correlation of various parameters, such as pH, electrical conductivity (EC), fluoride, chloride, etc., was studied with nitrate, and maximum correlation was found with chloride and EC.  Our prediction concentration of nitrate ion using Different Machine Learning (ML) algorithms, including Regression, Random Forest (RF), Support Vector Regression (SVR) and Decision Tree (DT) models using the input parameters as pH, EC chloride, and fluoride.  The result showcased that the best model is Support Vector Regression (SVR) with an R2 value of 0.93 and a Mean Square Error (MSE) value of 0.02 for the region. The region's nitrate pollution might be forecast using the SVR model for better estimation.