Water Quality Index Analysis and Prediction: A Case Study of Canals in Bangkok Thailand

Petchporn Chawakitchareon, Bernhard Thalheim, Yasushi Kiyoki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents a comparison of prediction methods for a water quality index (WQI) that is used for classification of water quality in rivers or canals. In this work, we consider the water quality index of two canals namely Phadung Krung Kasem Canal and Saen Saep Canal, Bangkok, Thailand as a case study. We compare results from M5P, M5Rules, REPTree with results from multilayer perceptron. The models employ five input variables including dissolved oxygen (DO), biological oxygen demand (BOD), ammonia nitrogen (NH3-N), Fecal Coliform bacteria (FCB) and Total Coliform bacteria (TCB) which were measured in the canals. The data in this research had been collected from Bangkok Metropolitan Authority, Thailand from 1 January 2007 to 31 November 2017. The total number of data is 2,000 records. The 10-fold cross validation method is used for evaluation of prediction models. It allows to determine the most effective method. Our experimental results show that the REPTree method yielded the highest accuracy to predict water quality index compared to other methods proposed in this paper.

Original languageEnglish
Title of host publicationInformation Modelling and Knowledge Bases XXXI
EditorsAjantha Dahanayake, Janne Huiskonen, Yasushi Kiyoki, Bernhard Thalheim, Hannu Jaakkola, Naofumi Yoshida
PublisherIOS Press
Pages419-429
Number of pages11
ISBN (Electronic)9781643680446
DOIs
Publication statusPublished - 2019 Dec 13
Event29th International Conference on Information Modeling and Knowledge Bases, EJC 2019 - Lappeenranta, Finland
Duration: 2019 Jun 32019 Jun 7

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume321
ISSN (Print)0922-6389

Conference

Conference29th International Conference on Information Modeling and Knowledge Bases, EJC 2019
CountryFinland
CityLappeenranta
Period19/6/319/6/7

Keywords

  • Artificial neural network
  • Data mining
  • M5p
  • M5rules
  • Machine learning
  • Mlp
  • Multilayer perceptron
  • Reptree
  • Water quality index
  • Weka
  • Wqi

ASJC Scopus subject areas

  • Artificial Intelligence

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  • Cite this

    Chawakitchareon, P., Thalheim, B., & Kiyoki, Y. (2019). Water Quality Index Analysis and Prediction: A Case Study of Canals in Bangkok Thailand. In A. Dahanayake, J. Huiskonen, Y. Kiyoki, B. Thalheim, H. Jaakkola, & N. Yoshida (Eds.), Information Modelling and Knowledge Bases XXXI (pp. 419-429). (Frontiers in Artificial Intelligence and Applications; Vol. 321). IOS Press. https://doi.org/10.3233/FAIA200028