Water Quality Classification by Integration of Attribute-Realization and Support Vector Machine for the Chao Phraya River

• Main Authors: Chalisa Veesommai Sillberg, Pratin Kullavanijaya, Orathai Chavalparit
• Format: Article
• Language: English
• Published: Polish Society of Ecological Engineering (PTIE) 2021-10-01
• Series: Journal of Ecological Engineering
• Subjects: environmental data analysis; machine learning; svm; water quality index; wqi
• Online Access: http://www.jeeng.net/Water-Quality-Classification-by-Integration-of-Attribute-Realization-and-Support,141364,0,2.html

The water quality index (WQI) is an essential indicator to manage water usage properly. This study aimed at applying a machine learning-based approach integrating attribute-realization (AR) and support vector machine (SVM) algorithm to classify the Chao Phraya River's water quality. The historical monitoring dataset during 2008-2019 including biological oxygen demand (BOD), conductivity (Cond), dissolved oxygen (DO), faecal coliform bacteria (FCB), total coliform bacteria (TCB), ammonia (NH3-N), nitrate (NO3-N), salinity (Sal), suspended solids (SS), total nitrogen (TN), total dissolved solids (TDS), and turbidity (Turb), were processed via four studied steps: data pre-processing by means substituting method, contributing parameter evaluation by recognition pattern study, examination of the mathematic functions for quality classification, and validation of obtained approach. The results showed that NH3-N, TCB, FCB, BOD, DO, and Sal were the main attributes contributing orderly to water quality classification with confidence values of 0.80, 0.79, 0.78, 0.76, 0.69, and 0.64, respectively. Linear regression was the most suitable function to river water data classification than Sigmoid, Radial basis and Polynomial. The different number of attributes and mathematic functions promoted the different classification performance and accuracy. The validation confirmed that AR-SVM was a potent approach application to classify river water's quality with 0.86-0.95 accuracy when applied three to six attributes.