Forecasting of water level in Urmia Lake using Time series, Artificial Neural Network and Neural Network-Wavelet

Urmia Lake in Iran is the second largest saline lake in the world. Due to various socio-economical and ecological criteria, Urmia Lake has important role in the Northwestern part of the country but it has faced many problems in recent years. Because of droughts, overuse of surface water resources and dam constructions water level is reduced. One of the important factors that has influence in correct management, is having a suitable point of view for future events in that field. So simulation and forecasting of hydrological variables has many importance. In this research, time series, Artificial Neural Network and Neural Network-Wavelet methods for presentation the best method in monthly scale for simulation and forecasting Urmia Lake water level is compared. Comparing these three methods indicates that forecasting with Neural Network-Wavelet due to considering monthly, seasonal and annual changes in the time series analysis, has the best Performance.