Comparison of Savran and Akdere flow measurement station datausing deep learning methods

Water is the most important source of life in human life for centuries. Due to the growing population, the need for water on earth is increasing day by day. In contrast to this increase, water resources; global warming, drought, climate changes, unplanned consumption is desiphering and losing sustainability. The forward forecast of river currents is of great importance in order to ensure sustainability. If the predictions are made correctly; major improvements can be made in the future management, operation, storage and correct use of water. The input-output account of the waters that have recently rened in rivers is predicted by forward-looking artificial intelligence techniques. The long-term estimates provide suitable planning for both the producer and the user for the production of water resources for living beings, irrigation, hydroelectric power generation and the transfer of water to future generations. In this study, the estimation of river currents is done by creating artificial neural network (ANN) and Deep Learning model from Artificial Intelligence (AI) techniques using numerical current data measured in flow measurement stations (FMS). Performance analyses were examined and evaluated using deep learning optimizers for many years of daily flow values of 2 different rasts in the Euphrates Basin, one of the 25 basins in Turkey. The best forecast model is determined by comparing actual data and forecast models. The highest correlation for Akdere and Göksu was determined at MAE values using ADAM and ADAMAX optimizers.