TOWARDS DEVELOPING A CLASSIFICATION MODEL FOR WATER POTABILITY IN PHILIPPINE RURAL AREAS

Abstract

In the Philippines, access to safe and sustainable water source is a major problem especially in rural areas. Thus, water monitoring in different water resources has been practiced to ensure safe drinking water. However, manual monitoring of safe drinking water is known to be inconvenient since it requires high operational and transportation costs, and time consuming. This study develops a data-driven water classification model for rural household areas using sensor nodes and machine learning algorithm. Sensor nodes are installed in several water sources in different rural areas to collect water parameters such as pH, turbidity, total dissolved solids, and temperature which are wirelessly transmitted to a base station. The collected sensor data is used to build and train the model to classify water potability using a hard-voting method in ensemble learning. The ensemble learning combined three machine learning algorithms namely k-nearest Neighbor, Naive Bayes, and Classification and Regression Tree. Finally, data are sent to a cloud for data storage and remote monitoring. Results show that the voting classifier model achieves an accuracy of 97% compared with other stand-alone classification algorithms. Furthermore, the model achieves 90% match with conventional industrial laboratory test.