Predicting Monthly Streamflow Using a Hybrid Wavelet Neural Network: Case Study of the Çoruh River Basin (original) (raw)

Polish Journal of Environmental Studies

Climate change, population growth, industrialization, and environmental impacts cause spatiotemporal changes in the availability of regional water resources [1, 2]. In particular, climate change will affect the streamflow, temperature, amount of precipitation, and variability, which are the main components of the hydrological cycle [3-5]. For example, Jiao and Wang [6] state in their study that the streamflow and rainfall are in a decreasing trend while the temperature is in an increasing trend in the last decades. Modeling and outlining streamflow is a crucial process in water management and planning, and accurate streamflow prediction is a vital tool for optimal water quantity and quality management [7]. Studies on accurate projections of temporal streamflow patterns can aid in understanding the properties of hydrological processes in basins and improving basin modeling [8]. Many studies have been conducted that examined the relationship of streamflow with precipitation and temperature, and evaluated its changes and forecasts [9, 10]. Xu et al. [11] found that periodic changes in streamflow were closely correlated with temperature