Classification of Drinking Water Quality using Support Vector Machine (SVM) Algorithm

Abstract

Water is extremely important in both the environmental and social realms. The consumption of clean water guarantees a quality of life as it provides essential minerals and nutrients to the body. Water pollution posing a threat to human health, ecosystems, plant, and animal life. Today, Malaysia is showing an increasing rate of water pollution as there are currently undergoing tremendous urbanization and population expansion. The Water Quality Index (WQI) must monitor frequently to ensure the level of water cleanliness and safeness. However, monitoring work was conduct manually are time consuming, requires a lot of manpower and high expertise in determining the level of water cleanliness. Due to those issues, the intention of this study is to develop an automatic method in water quality classification for drinking purpose whether it is potable or non-potable using Support Vector Machine (SVM) which is more accurate, fast, and easy. This project used up to 59 samples of data from various location to prepare the SVM with two different kernels. By using MATLAB version R2021A, the implementation of this project was performed. Based on the result obtained, it is discovered that SVM model with RBF kernel has the better performance with high percentage of accuracy, precision, sensitivity, and specificity compared to SVM model with Polynomial kernel. All two types of kernels were accepted to be used in SVM model water quality classifier as their performance's criteria which are accuracy, specificity, sensitivity, and precision were greater than 80%. The findings of the study were benefits to the other or future work, particularly in the water quality classification system.