Artificial Neural Network Modelling of Total Dissolved Solid (Elzawia city – Libya, as a case study) - Ben Taher, Lubna (Libyan Journal for Engineering Research, 2017)
In this study Mathematical, and statistical methods to simulate an aquifer water quality parameters have been considered. It is necessary to measured water quality for groundwater zonation maps. Therefore, more accurate maps produced more essential role in the discussionmanagement. Water samples were collected from thirty wells at Elzawi city located around 45km west of Tripoli and analysed for water quality parameters including: EC(Electrical Conductivity), TDS(Total Dissolved Solids), Ca (Calcium), Mg (Magnesium), and pH using standard methods. The wells location andwater level at wells observed by Global positioning system (GPS) type Garmin GPS 12XL. An artificial neural network (ANN) models were investigated to predict the TDS in water of Elzawi city wells. The input variables were the wells longitude, latitude, EC, Ca, Mg, and pH while the TDS in water was the output. The Levenberg–Marquardt (LM) algorithm and Back propagation used for training of the feed forward ANN. The ANN models performance were compared using the coefficient of determination(E), mean absolute percentage error (MAPE %), and 95% confidence interval(CI95%).It has been a good agreement between actual data and the ANN outputs for training, validation and testing data sets at the forth model (ANN4) while based on all inputs . ANN4 model performed superior to the other models in predicting TDS with high E=0.94 and lowest MAPE= 5.6% and the result average within the range of observed 95CI%. Also the ANN models could be successfully applied and provide high accuracy and reliability for water quality parameters forecasting.