Abstract: The use of artificial neural network (ANN) modeling
for prediction and forecasting variables in water resources
engineering are being increasing rapidly. Infrastructural applications
of ANN in terms of selection of inputs, architecture of networks,
training algorithms, and selection of training parameters in different
types of neural networks used in water resources engineering have
been reported. ANN modeling conducted for water resources
engineering variables (river sediment and discharge) published in
high impact journals since 2002 to 2011 have been examined and
presented in this review. ANN is a vigorous technique to develop
immense relationship between the input and output variables, and
able to extract complex behavior between the water resources
variables such as river sediment and discharge. It can produce robust
prediction results for many of the water resources engineering
problems by appropriate learning from a set of examples. It is
important to have a good understanding of the input and output
variables from a statistical analysis of the data before network
modeling, which can facilitate to design an efficient network. An
appropriate training based ANN model is able to adopt the physical
understanding between the variables and may generate more effective
results than conventional prediction techniques.
Abstract: Two short sediment cores collected from mangrove
areas of Manori and Thane creeks along Mumbai coast were analysed
for sediment composition and metals (Fe, Mn, Cu, Pb, Co, Ni, Zn, Cr
and V). The statistical analysis of Pearson correlation matrix proved
that there is a significant relationship between metal concentration
and finer grain size in Manori creek while poor correlation was
observed in Thane creek. Based on the enrichment factor, the present
metal to background metal ratios clearly reflected maximum
enrichment of Cu and Pb in Manori creek and Mn in Thane creek.
Geoaccumulation index calculated indicate that the study area is
unpolluted with respect to Fe, Mn, Co, Ni, Zn and Cr in both the
cores while moderately polluted with Cu and Pb in Manori creek.
Based on contamination degree, both the core sediments were found
to be considerably contaminated with metals.