Water Quality Classification Using SVM And XGBoost Method
Hasriq Izzuan Hasnol Yusri, A’zraa Afhzan Ab Rahim, Siti Lailatul Mohd Hassan, Ili Shairah Abdul Halim, Noor Ezan Abdullah
Abstract
Various pollutants have been endangering water quality over the past decades. As a result, predicting and modeling water quality have become essential to minimizing water pollution. This research has developed a classification algorithm to predict the water quality classification (WQC). The WQC is classified based on the water quality index (WQI) from 7 parameters in a dataset using Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost). The results from the proposed model can accurately classify the water quality based on their features. The research outcome demonstrated that the XGBoost model performed better, with an accuracy of 94%, compared to the SVM model, with only a 67% accuracy. Even better, the XGBoost resulted in only 6% misclassification error compared to SVM, which had 33%. On top of that, XGBoost also obtained consistent superior results from 5-fold validation with an average accuracy of 90%, while SVM with an average accuracy of 64%. Considering the enhanced performance, XGBoost is concluded to be better at water quality classification.