Comparison of Recurrent Neural Network, Adaptive Neuro-Fuzzy Inference System and Stochastic Models in Egirdir Lake Level Forecasting

Accurate prediction of lake-level changes is a very important problem for a wise and sustainable use. In recent years significant lake level fluctuations have occurred and can be related to the climatic change. Such a problem is crucial to the works and decisions related to the water resources and management. This study is aimed to predict future lake levels during hydrometeorological changes and anthropogenic activities taking place in the Lake Egirdir which is the most important water storage of Lake Region, one of the biggest fresh water lakes of Turkey. For this aim, recurrent neural network (RNN), adaptive network-based fuzzy inference system (ANFIS) as prediction models which have various input structures were constructed and the best fit model was investigated. Also, the classical stochastic models, auto-regressive (AR) and auto-regressive moving average (ARMA) models are generated and compared with RNN and ANFIS models. The performances of the models are examined with the form of numerical and graphical comparisons in addition to some statistic efficiency criteria. The results indicated that the RNN and ANFIS can be applied successfully and provide high accuracy and reliability for lake-level changes than the AR and the ARMA models. Also it was shown that these stochastic models can be used in the lake management policies with the acceptable risk.