TY  - JOUR
T1  - Comparison of Artificial Neural Network Algorithm for Water Quality Prediction of River Ganga
AU - Giri, Aradhana AU - Singh, N.B. 
JO  - Environmental Research Journal
VL  - 8
IS  - 2
SP  - 55
EP  - 63
PY  - 2014
DA  - 2001/08/19
SN  - 1994-5396
DO  - erj.2014.55.63
UR  - https://makhillpublications.co/view-article.php?doi=erj.2014.55.63
KW  - Artificial Neural Networks (ANN)
KW  -Lavenberg Marquardt (LM)
KW  -Gradient Descent Adaptive (GDA)
KW  -River Ganga
KW  -water
AB  - The development of any region depends greatly on the availability 
  of appropriate water supplies. The quality of water can be judged based on a 
  variety of parameters among which the most important is the temperature. In 
  this study, Artificial Neural Network algorithms, Lavenberg Marquardt (LM) and 
  Gradient Descent Adaptive (GDA) have been used to predict the quality of water. 
  Using the data of temperature for the year 2008 to 12, researchers have measured 
  Biochemical Oxygen Demand (BOD) and Dissolved Oxygen (DO) along River Ganga. 
  Both the algorithms, mentioned above, have been compared for their performance. 
  The results show that the algorithm LM gives a better performance as compared 
  to that of GDA. Hence, simulated values for the desired locations at which measured 
  data are unavailable can be efficiently provided by a trained ANN Model.
ER  -