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.
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Acknowledgments
This study was funded by the Scientific and Technological Research Council of Turkey (TÜBİTAK project no: 104Y188) and the Scientific Research Projects Committee of Harran University (HÜBAK project no: 603). The authors would like to thank Yasemin Bayindir, Ozlem Demir, Atiye Atguden and Nuray Gok for their continuous help in the field and laboratory studies.
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Yesilnacar, M.I., Sahinkaya, E., Naz, M. et al. Neural network prediction of nitrate in groundwater of Harran Plain, Turkey. Environ Geol 56, 19–25 (2008). https://doi.org/10.1007/s00254-007-1136-5
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DOI: https://doi.org/10.1007/s00254-007-1136-5