Water Quality Prediction Using Machine Learning

Abstract

Different toxins have been imperiling water quality over the past decades. As a result, foreseeing and modeling water quality have gotten to be basic to minimizing water contamination. This inquiry has created a classification calculation to foresee the water quality classification (WQC). The WQC is classified based on the water quality file (WQI) from 7 parameters in a dataset utilizing Back Vector Machine (SVM) and Extraordinary Gradient Boosting (XGBoost). The comes about from the proposed model can precisely classify the water quality based on their features. The inquire about result illustrated that the XGBoost model performed way better, with an exactness of 94%, compared to the SVM demonstrate, with as it were a 67% exactness. Indeed way better, the XGBoost brought about in as it were 6% misclassification mistake compared to SVM, which had 33%. On best of that, XGBoost too gotten consistent predominant comes about from 5-fold approval with an normal accuracy of 90%, whereas SVM with an normal exactness of 64%. Considering the upgraded execution, XGBoost is concluded to be superior at water quality classification.