Application of artificial neural network for forecasting nitrate concentration as a water quality parameter: A case study of Feitsui Reservoir, Taiwan

Sarmad Dashti Latif, Muhammad Shukri Bin Nor Azmi, Ali Najah Ahmed, Chow Ming Fai, Ahmed El-Shafie

Research output: Contribution to journalArticleResearchpeer-review

5 Citations (Scopus)

Abstract

Water resources play a vital role in various economies such as agriculture, forestry, cattle farming, hydropower generation, fisheries, industrial activity, and other creative activities, as well as the need for drinking water. Monitoring the water quality parameters in rivers is becoming increasingly relevant as freshwater is increasingly being used. In this study, the artificial neural network (ANN) model was developed and applied to predict nitrate (NO3) as a water quality parameter (WQP) in the Feitsui reservoir, Taiwan. For the input of the model, five water quality parameters were monitored and used namely, ammonium (NH3), nitrogen dioxide (NO2), dissolved oxygen (DO), nitrate (NO3) and phosphate (PO4) as input parameters. As a statistical measurement, the correlation coefficient (R) is used to evaluate the performance of the model. The result shows that ANN is an accurate model for predicting nitrate as a water quality parameter in the Feitsui reservoir. The regression value for the training, testing, validation, and overall are 0.92, 0.93, 0.99, and 0.94, respectively.

Original languageEnglish
Pages (from-to)647-652
Number of pages6
JournalInternational Journal of Design & Nature and Ecodynamics
Volume15
Issue number5
DOIs
Publication statusPublished - Oct 2020
Externally publishedYes

Keywords

  • Artificial neural network (ANN)
  • Feitsui reservoir
  • Nitrate concentration
  • Water quality parameter

Cite this