Abstract:
In this study, an automatic relevance determination-based
Bayesian neural network (ARD-BNN) approach
is employed combing hydro-geochemical and geo-electrical
information to predict groundwater quality index (GWQI) of
coastal Maharashtra, India. In the first step, to incorporate
geo-electrical information for GWQI forward computation,
the weight corresponding to true resistivity of the earth layer
is estimated via ARD-BNN modelling using training sample
generated for groundwater classification. In the second step,
incorporating earth resistivity weight information, a total of
1500 training samples are created honouring World Health
Organisation (WHO) guidelines for GWQI prediction. Prior
to actual data analysis, we explored the algorithm on GWQI
variable series assorted with different level of complex (red)
noise to examine the bounds of network hyper-parameter.
What distinguishes our approach from previous approach for
ARD-BNN optimization is that we seek to develop a mechanism
which allows specific weight estimation and provides
insight into which hyper parameters and their bounds are
appropriate to predict GWQI from noise intervened GWQI
data. The model shows excellent performance between predicted
and computed GWQI both in training (trn) and test
(tst) data with Pearson’s correlation coefficient (rtrn ~ 0.91
and rtst ~ 0.90), root-mean-squared-error (RMSE) error
(RMSEtrn~ 1.2 and RMSEtst~ 1.4), reduction of error (RE)
(REtrn~0.98 and REtst~0.97), and index of agreement (IA) (IAtrn~0.95 and IAtst~0.93). Red noise analysis shows that
the ARD-BNN model is robust up to the noise level of 20%
in the input variable for GWQI prediction. The network
adopted relevance analysis to indicate the relative importance
of input parameter in the prediction of GWQI via
ARD-BNN modelling which showed that chloride [Cl−],
[pH], [HCO3
−] and sodium [Na+] are dominant while considering
various simulations for characterizing the GWQI
of coastal Maharashtra. The approach used here, could be
useful in understanding the relative contribution and/or
modelling pollution source in many other environmental
applications.
