Monitoring of groundwater quality is one of the important tools to provide adequate information about water management. In the present study, artificial neural network (ANN) with a feed-forward back-propagation was designed to predict groundwater salinity, expressed by total dissolved solids (TDS), using pH as an input parameter. Groundwater samples were collected from a 36. m depth well located in the experimental farm of the City of Scientific Researches and Technological Applications (SRTA City), New Borg El-Arab City, Alexandria, Egypt. The network structure was 1-5-3-1 and used the default Levenberg-Marquardt algorithm for training. It was observed that, the best validation performance, based on the mean square error, was 14819 at epoch 0, and no major problems or over-fitting occurred with the training step. The simulated output tracked the measured data with a correlation coefficient (R-value) of 0.64, 0.67 and 0.90 for training, validation and test, respectively. In this case, the network response was acceptable, and simulation could be used for entering new inputs.
- Artificial neural network
ASJC Scopus subject areas
- Aquatic Science
- Water Science and Technology
- Ecology, Evolution, Behavior and Systematics