Development of In-situ Sensing System and Classification of Water Quality using Machine Learning Approach

Abstract

Quality of water applied to the agriculture sector is one of the factors for agriculture farming to be successful. The use of bad quality irrigation water can cause soil problems. In general, determining water quality model is one of the many interests as it can be used to classify the conditions of water. This project focuses on developing the in-situ sensing system of water quality sensors that can detect parameters of water quality such as pH level, electric conductivity, temperature and total dissolved solid. To validate the approach, there are three types of water samples in a dataset that was collected which include water pipes, soap water and drain water. The types of machine learning models used for classification process are Artificial Neural Network (ANN), Support Vector Machine (SVM), and Decision Tree. The performance showed that SVM model was the highest, ANN was intermediate, and Decision Tree was the lowest. This shows that the SVM model of machine learning approach is the most suitable to be used as the classification model to classify the status of water quality.