Due to the stochastic nature and complexity of flow, as well as the existence of hydrological uncertainties, predicting streamflow in dam reservoirs, especially in semi-arid and arid areas, is essential for the optimal and timely use of surface water resources. In this research, daily streamflow to the Ermenek hydroelectric dam reservoir located in Turkey is simulated using deep recurrent neural network (RNN) architectures, including bidirectional long short-term memory (Bi-LSTM), gated recurrent unit (GRU), long short-term memory (LSTM), and simple recurrent neural networks (simple RNN). For this purpose, daily observational flow data are used during the period 2012–2018, and all models are coded in Python software programming language. Only delays of streamflow time series are used as the input of models. Then, based on the correlation coefficient (CC), mean absolute error (MAE), root mean square error (RMSE), and Nash–Sutcliffe efficiency coefficient (NS), results of deep-learning architectures are compared with one another and with an artificial neural network (ANN) with two hidden layers. Results indicate that the accuracy of deep-learning RNN methods are better and more accurate than ANN. Among methods used in deep learning, the LSTM method has the best accuracy, namely, the simulated streamflow to the dam reservoir with 90% accuracy in the training stage and 87% accuracy in the testing stage. However, the accuracies of ANN in training and testing stages are 86% and 85%, respectively. Considering that the Ermenek Dam is used for hydroelectric purposes and energy production, modeling inflow in the most realistic way may lead to an increase in energy production and income by optimizing water management. Hence, multi-percentage improvements can be extremely useful. According to results, deep-learning methods of RNNs can be used for estimating streamflow to the Ermenek Dam reservoir due to their accuracy.

Solubility of salts and leaching fractions have different effects on drainage water quality. Knowing the quality of drainage water is extremely important in terms of environmental factors and quality of water resources for the reason that this water is transferred to various sources. In this study, were studied the changes of drainage water salinity and salt load under lyzimeter (soil columns) conditions using different irrigation water salinity and with different leaching fractions. The study was carried out with sunflower in PVC soil columns with 40 cm diameter and 115 cm length with 3 different irrigations and 5 irrigation waters with different salinity level. The three irrigation treatments were 75%, 115% and 135% of the required irrigation water. The irrigation water salinities were 0.25 dS m-1 as control treatment, 1.5 and 3.0 dS m-1 with NaCl+CaCl2 salts and 1.5 and 3.0 dS m-1 with NaCl+CaSO4 salts as saline treatments. In this study investigated drainage water quality variations and salt load with irrigation water and some individual ions load and their leaching by drainage water as well. Drainage water salinities variated with both irrigation water salinity and leaching fractions. It was higher under the effect of soluble salts and with the 15% leaching fraction. However, salt load was higher at 35% leaching fractions level. When discuss the individual ions; while Cl-, (CO3-2+HCO3-) and Ca+2 were accumulated in the soil profile, SO4-2, Na+ ve Mg+2 were leaced from the profile, and all these ions variated by interaction for irrigation waters, and by leaching fractions for the drainage water.