Authors :
Prerna Sengar; Jay Singh Rajput; A. K. Saxena
Volume/Issue :
Volume 7 - 2022, Issue 7 - July
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3zYMVzh
DOI :
https://doi.org/10.5281/zenodo.6969533
Abstract :
In India, river qualities are getting degraded
diurnally. Actually, it is due to dumping of waste,
discharging of untreated water and industrial waste etc.
in to the river. Consequently, the river gets contaminated
due to such anthropogenic activities. In this regard,
Water quality index (WQI) is widely utilized to monitor
the quality of river. WQI is a single unique number
which represent the quality status of river water. In this
study, a marking system based WQI has been developed
for Chambal River. In which, the permissible limits
(mentioned in IS-Code) of each water quality parameters
have been utilized for developing sub-Indices.
Subsequently, an average operator has been applied to
agglomerate the sub-indices in to a single number
termed as WQI. To achieve this, water quality
parameter data has been imported from Central
pollution control board database that composed the
concentration of Bio-Chemical Oxygen demand (B.O.D),
Dissolved Oxygen (D.O.), Conductivity, pH, Nitrate,
Total Coliform, and Faecal Coliform of 10 different
location of Chambal River. Additionally, a prediction
model also has been developed by using Artificial Neural
Network with artificial dataset. An artificial dataset was
generated by utilizing the actual dataset with random
sampling. In this direction, Levenberg Marquardt (LM),
Baysian Regularization (BR), and Scaled Conjugate
Gradient (SCG) algorithms were trained and tested with
different settings of hyper-parameter. As a result,
Keywords :
Water quality index (WQI), Artificial Neural Network (ANN), Chambal River.
In India, river qualities are getting degraded
diurnally. Actually, it is due to dumping of waste,
discharging of untreated water and industrial waste etc.
in to the river. Consequently, the river gets contaminated
due to such anthropogenic activities. In this regard,
Water quality index (WQI) is widely utilized to monitor
the quality of river. WQI is a single unique number
which represent the quality status of river water. In this
study, a marking system based WQI has been developed
for Chambal River. In which, the permissible limits
(mentioned in IS-Code) of each water quality parameters
have been utilized for developing sub-Indices.
Subsequently, an average operator has been applied to
agglomerate the sub-indices in to a single number
termed as WQI. To achieve this, water quality
parameter data has been imported from Central
pollution control board database that composed the
concentration of Bio-Chemical Oxygen demand (B.O.D),
Dissolved Oxygen (D.O.), Conductivity, pH, Nitrate,
Total Coliform, and Faecal Coliform of 10 different
location of Chambal River. Additionally, a prediction
model also has been developed by using Artificial Neural
Network with artificial dataset. An artificial dataset was
generated by utilizing the actual dataset with random
sampling. In this direction, Levenberg Marquardt (LM),
Baysian Regularization (BR), and Scaled Conjugate
Gradient (SCG) algorithms were trained and tested with
different settings of hyper-parameter. As a result,
Keywords :
Water quality index (WQI), Artificial Neural Network (ANN), Chambal River.
