Modeling of groundwater level using artificial intelligence techniques: A case study of Reyhanli region in Turkey

Abstract

Determination of the change in groundwater level in terms of planning and managing resources is important. In this study, the groundwater level of Reyhanli region in Turkey was predicted using multi-linear regression (MLR), adaptive neural fuzzy inference system (ANFIS), Radial basis neural network (RBNN), support vector machines with radial basis functions (SVM-RBF) and support vector machines with poly kernels (SVM-PK) methods. Models were carried out using 192 data of monthly ground water level, monthly total precipitation and monthly average temperature values measured for 16 years between 2000 and 2015. Comparisons revealed that the SVM-RBF and SVM-PK models had the most accuracy in the groundwater level prediction.