Development of a short-term water quality prediction model for urban rivers using real-time water quality data

J. H. Lee, J. Y. Lee, M. H. Lee, M. Y. Lee, Y. W. Kim, J. S. Hyung, K. B. Kim, Y. K. Cha, J. Y. Koo

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

We developed a classification model and a real-time prediction model for short-term dissolved oxygen (DO) at the junction of the Han River in Anyangcheon, where water quality accidents occur frequently. The classification model is an analysis model that derives the main factors affecting DO changes in the Anyangcheon mobile water quality monitoring network using decision tree, random forest, and XGBoost. The model identified the key factors affecting DO changes to be electrical conductivity, cumulative precipitation, total nitrogen, and water temperature. Random forest (sensitivity, 0.9962; accuracy, 0.9981) and XGBoost (sensitivity, 1.0000; accuracy, 0.9822) showed excellent classification performance. The real-time prediction model for short-term DO that we developed adopted artificial neural network (ANN), long short-term memory (LSTM), and gated recurrent unit (GRU) algorithms. LSTM (R2 = 0.93 - 0.97, first half; R2 = 0.95 - 0.96, second half) and GRU (R2 = 0.94 - 0.98, first half; R2 = 0.96 - 0.98, second half) significantly outperformed ANN (R2 = 0.64 - 0.86). The LSTM and GRU models we developed used real-time automatic measurement data, targeting urban rivers that are sensitive to water quality changes and are waterfront areas for citizens. They can quickly reflect and simulate short-term, real-time changes in water quality compared with existing static models.

Original languageEnglish
Pages (from-to)4082-4097
Number of pages16
JournalWater Supply
Volume22
Issue number4
DOIs
StatePublished - 1 Apr 2022

Keywords

  • classification model
  • dissolved oxygen prediction model
  • real-time automatic measurement data
  • urban river
  • water quality accident

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