Efficient river water quality index prediction considering minimal number of inputs variables

Faridah Othman, M. E. Alaaeldin, Mohammed Seyam, Ali Najah Ahmed, Fang Yenn Teo, Chow Ming Fai, Haitham Abdulmohsin Afan, Mohsen Sherif, Ahmed Sefelnasr, Ahmed El-Shafie

Research output: Contribution to journalArticlepeer-review

58 Citations (Scopus)


Water Quality Index (WQI) is the most common determinant of the quality of the stream-flow. According to the Department of Environment (DOE, Malaysia), WQI is chiefly affected by six factors, which are, chemical oxygen demand (COD), biochemical oxygen demand (BOD), dissolved oxygen (DO), suspended solids (SS), -potential for hydrogen (pH), and ammoniacal nitrogen (AN). In fact, understanding the inter-relationships between these variables and WQI can improve predicting the WQI for better water resources management. The aim of this study is to create an input approach using ANNs (Artificial Neural Networks) to compute the WQI from input parameters instead of using the indices of the parameters when one of the parameters is absent. The data are collected from the nine water quality monitoring stations at the Klang River basin, Malaysia. In addition, comprehensive sensitivity analysis has been carried out to identify the most influential input parameters. The model is based on the frequency distribution of the significant factors showed exceptional ability to replicate the WQI and attained very high correlation (98.78%). Furthermore, the sensitivity analysis showed that the most influential parameter that affects WQI is DO, while pH is the least one. Additionally, the performance of models shows that the missing DO values caused deterioration in the accuracy.

Original languageEnglish
Pages (from-to)751-763
Number of pages13
JournalEngineering Applications of Computational Fluid Mechanics
Issue number1
Publication statusPublished - Jan 1 2020


  • Artificial Neural Networks
  • Surface water hydrology
  • modelling
  • water quality index

ASJC Scopus subject areas

  • General Computer Science
  • Modelling and Simulation


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