Water quality classification using neural networks: Case study of canals in Bangkok, Thailand

Abstract

Water quality is one of the major concerns of countries around the world. This study endeavors to automatically classify water quality. The water quality classes are evaluated using 3 chemical factor indices. These factors are pH value (pH), Dissolved Oxygen (DO), and Biochemical Oxygen Demand (BOD). The methodology involves applying data mining techniques using neural networks with the Levenberg-Marquardt algorithm on data from 288 canals in Bangkok, Thailand. The data is obtained from the Department of Drainage and Sewerage Bangkok Metropolitan Administration during 2003–2007. The results exhibit a high accuracy rate at 99.34% in classifying the water quality of canals in Bangkok. Subsequently, this encouraging result could be applied with more parameters and also can be extended to the related science.