Forecasting discharge rate and chloride content of karstic spring water by applying the Levenberg–Marquardt algorithm

The Mediterranean countries' coastal-karstic aquifer systems are facing increased pressure due to seawater intrusion and drought impacts. There is a need to understand better karstic systems' functional mechanisms for developing the most appropriate management scenario of water resources in these systems. In this study, the application of Non-Linear Autoregressive neural networks (NAR) on a dataset from mid-1968 to 1994 was deployed for predicting values of discharge flow rates and salinity of Almyros spring (Heraklion-Crete, Greece). Two neural networks were trained for the prediction of the discharge rates and the chloride concentration. The neural networks operated with the Levenberg–Marquardt algorithm's aid and attained a coefficient of determination R = 0.83, and R = 0.86, respectively, indicating a high degree of prediction capacity.