『Abstract
Monitoring groundwater quality by cost-effective techniques is
important as the aquifers are vulnerable to contamination from
the uncontrolled discharge of sewage, agricultural and industrial
activities. Faulty planning and mismanagement of irrigation schemes
are the principle reasons of groundwater quality deterioration.
This study presents an artificial neural network (ANN) model predicting
concentration of nitrate, the most common pollutant in shallow
aquifers, in groundwater of Harran Plain. The samples from 24
observation wells were monthly analysed for 1 year. Nitrate was
found in almost all groundwater samples to be significantly above
the maximum allowable concentration of 50 mg/L, probably due to
the excessive use of artificial fertilizers in intensive agricultural
activities. Easily measurable parameters such as temperature,
electrical conductivity, groundwater level and pH were used as
input parameters in the ANN-based nitrate prediction. The best
back-propagation(BP) algorithm and neuron numbers were determined
for optimization of the model architecture. The Levenberg-Marquardt
algorithm was selected as the best of 12 BP algorithms and optimal
neuron number was determined as 25. The model tracked the experimental
data very closely (R = 0.93). Hence, it is possible to manage
groundwater resources in a more cost-effective and easier way
with the proposed model application.
Keywords: Groundwater quality; Harran Plain; GAP project; Nitrate;
Artificial neural network』
Introduction
Description of the study area
Analytical methods
Modelling
Selection of back-propagation algorithm
Optimisation of neuron number
Model results and discussion
Conclusions
Acknowledgments
References