| en |
Simulation, evaluation and prediction modeling of river water quality properties (case study: Ireland Rivers)
Salami, E. S. & Ehteshami, M.
Abstract
In this analysis, three input parameters temperature,
pH and electrical conductivity were chosen due to
their easy and less costly measurement technique, and a
package of six models were presented for estimating the
concentrations of dissolved oxygen, DO percentage, biological
oxygen demand, chloride, alkalinity and total
hardness. 3001 data sets (a 3001 × 8 data array) were used
to training the models. The models have been tested in
order to verify their prediction values, and the resulted
R factor (the rate of precision) for each model equals to
0.93, 0.95, 0.77, 0.82, 0.85 and 0.92, respectively. This
proves that the package can be used to estimate the concentrations
of water quality parameters with accuracy close
to the reality. The River data collected from 210
monitoring stations located in all over Ireland have been
used. The data set covers different conditions and makes
the model applicable in many different places and conditions.
For development of all models, feed-forward algorithm
used for training, as well as the Levenberg–
Marquardt and tansign(x) functions as learning and transfer
functions.
Keywords
Artificial neural networks; Ireland Rivers; Modeling; Water characteristics; Water quality
|
