TY - JOUR
T1 - Application of artificial neural network for forecasting nitrate concentration as a water quality parameter
T2 - A case study of Feitsui Reservoir, Taiwan
AU - Latif, Sarmad Dashti
AU - Azmi, Muhammad Shukri Bin Nor
AU - Ahmed, Ali Najah
AU - Fai, Chow Ming
AU - El-Shafie, Ahmed
N1 - Publisher Copyright:
© 2020 WITPress. All rights reserved.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/10
Y1 - 2020/10
N2 - 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.
AB - 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.
KW - Artificial neural network (ANN)
KW - Feitsui reservoir
KW - Nitrate concentration
KW - Water quality parameter
UR - http://www.scopus.com/inward/record.url?scp=85096549010&partnerID=8YFLogxK
U2 - 10.18280/ijdne.150505
DO - 10.18280/ijdne.150505
M3 - Article
AN - SCOPUS:85096549010
SN - 1755-7437
VL - 15
SP - 647
EP - 652
JO - International Journal of Design & Nature and Ecodynamics
JF - International Journal of Design & Nature and Ecodynamics
IS - 5
ER -