Title

Towards developing a classification model for water potability in Philippine rural areas

College

Gokongwei College of Engineering

Department/Unit

Electronics And Communications Engg

Document Type

Article

Source Title

ASEAN Engineering Journal

Volume

10

Issue

2

First Page

24

Last Page

34

Publication Date

6-1-2020

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. © 2020 ASEAN University Network/Southeast Asia Engineering Education Development Network. All right reserved.

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Disciplines

Electrical and Computer Engineering

Keywords

Drinking water--Philippines--Testing; Ensemble learning (Machine learning)

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