Neural network based modeling of environmental variables: A summary of successful applications

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20 Οκτ 2013 (πριν από 3 χρόνια και 9 μήνες)

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Neural network based modeling of environmental variables: A
summary of successful applications


The Boreal Plain ecozone of the Canadian Boreal Forest is experiencing both natural,
mainly wildfires, and anthropogenic, primarily forest harvesting, watershed

disturbances.
Such disturbances can significantly alter the timing and intensity of water, nutrients, and
sediment outputs from these watersheds. A measurable increase in nutrient loading to
water bodies may increase dissolved oxygen depletion, cyanobacte
ria biomass formation,
and cyanobacterial toxin production potentially disrupting fish habitat and possibly
deteriorating the performance of downstream water treatment plants. Therefore, nutrient
and sediment modelling, is critical to the protection of aqu
atic ecosystems and the
preservation of source water quality. Most of the currently available m
odels, for
waters
hed modeling, are undermined in practice because of the extensive landscape data
required for model calibration. However, satellite remote sensi
ng (RS) has recently made
available cost
-
effective data over the entire landscape rather than providing a sampling of
it, as would be the case w
ith ground
-
based measurements.
This study proposed an
artificial neural network (ANN) modelling algorithm that r
elies on low cost readily
available meteorological data

as well as RS information

for simulating streamflow (
Q
),
total suspended solids (
TSS
) concentration, and total phosphorus (
TP
) concentration. The

developed

models were applied to
four forested watersh
eds

in the Canadian Boreal Plain.
Our results demonstrated that through careful manipulation of time series analysis and
rigorous optimization of ANN configuration, it is possible to simulate
Q
,
TSS
, and
TP

reasonably well. R
2

values exceeding 0.8 were obt
ained for all modelled data cases. The
proposed models can provide real time predictions of the modelled parameters, can
answer questions related to the impact of climate change scenarios on water quantity and
quality, and can be implemented in water resou
rces management through Monte Carlo
simulations.