Classification model for water quality using machine learning techniques

Azilawati, Rozaimee and Azrul Amri, Jamal and Azwa, Abdul Aziz (2015) Classification model for water quality using machine learning techniques. International Journal of Software Engineering and its Applications, 9 (6). pp. 45-52. ISSN 17389984 [P]

Image FH02-FIK-15-03717.jpg Restricted to Registered users only Download (163kB)

Abstract

The problem of water pollution is increasing every day, due to the industries’ waste product disposal, migration of people from rural to urban areas, crowded population, untreated sewage disposal, wastewater and other harmful chemicals’ discharge from the industries. There is a need to resolve this problem for us to get good water that can be used for domestic purposes. This article proposes a suitable classification model for classifying water quality based on the machine learning algorithms. The paper analyzed and compared performance of various classification models and algorithms in order to identify the significant features that contributed in classifying water quality of Kinta River, Perak Malaysia. Five models with respective algorithms were tested and compared with their performance. In assessing the result, the Lazy model using K Star algorithm was the best classification model among the five models had the most outstanding accuracy of 86.67%. Generally, wastewater is harmful to our lives, and bringing scientific models in solving this problem is obligatory.

Actions (login required)

View Item