How to cite: Y. Z. KAYA, F. ÜNEŞ, M. DEMIRCI, B. TAŞAR, H. VARÇİN (2018) Groundwater Level Predıctıon Usıng Artıfıcıal Neural Network and M5 Tree Models
2018 Air and Water Components of the Environment Conference Proceedings, P. 195-201, DOI: 10.24193/AWC2018_23
GROUNDWATER LEVEL PREDICTION USING ARTIFICIAL NEURAL NETWORK AND M5 TREE MODELS
Y. Z. KAYA, F. ÜNEŞ, M. DEMIRCI, B. TAŞAR, H. VARÇİN
ABSTRACT. Groundwater Level Predıctıon Usıng Artıfıcıal Neural Network and M5 Tree Models. Most of the fresh water resources in our world consist of underground water reserves. Estimation of fluctuations of groundwater level (GWL) is very important in the management of water resources. In this study, groundwater level (GWL) was investigated using artificial neural networks (ANN), M5tree (M5T) approaches in Reyhanlı region in Turkey. Total 196 data from 2000-2015 taken from 1 observation station belonging to Reyhanlı sub-basin located in Asi basin were used in the study. Using the monthly average precipatation and temperature, the change in GWL is modeled by artificial neural networks (ANN), M5tree (M5T) approaches. The results showed that (ANN) and M5tree (M5T) models were found to be very close to each other.
Keywords: limnosphere, limnic water, limnosystem.