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    Evaluation of the Static and Pseudo-Static Stability and Effectiveness of an Improvement Technique for Slopes of the Vanyar Dam Reservoir
    (Korean Society Of Civil Engineers-Ksce, 2021) Ahbab, Amirhossein; Akhlaghi, Tohid; Safari, Mir Jafar Sadegh; Avcı, Eyübhan
    Excavation on the inclined surfaces on the dam reservoir and rising water level may also affect the slope stability of the inclined surfaces in the dam reservoir under static and dynamic conditions. In this study, it is aimed to present a three-dimensional (3D) model to analyze slope stability of access road to the dam's crest and calculates the value of FOS in process of instructing and exploitation of dam and estimating the possibility of landslide occurrence during excavations and impounding of the dam. To this end, analysis of the slope stability has been implemented based on the information obtained from the field inspections, investigations, geological surveys, manual and mechanical borings in laboratory and field experiments. For acquiring the value of factor of safety (FOS), an explicit-finite-difference code is implemented. Effects of excavations in different levels of slope and fluctuation of water table in instability of the slope have been analyzed. The outcomes reveal that through increasing the level of water, FOS is decreased and large amounts of soil were entered in the dam's reservoir, blocking the entrance of the drainage valve and disrupt access way to the dam crest. Therefore, piles in the different distance have been used for controlling the slope stability and the best distribution of piles based on acceptable values for factor of safety in different regulations have been determined. It was observed that the excavation on the slope and increment of the water level in the dam reservoir influence the slope stability.
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    A stochastic approach for the assessment of suspended sediment concentration at the Upper Rhone River basin, Switzerland
    (Springer, 2022) Vaheddoost, Babak; Vazifehkhah, Saeed; Safari, Mir Jafar Sadegh
    This study addresses the link between suspended sediment concentration, precipitation, streamflow, and direct runoff components. This is important since suspended sediment concentration in the streamflow has invaluable importance in the management of the river basin. For this, the daily streamflow time series in five consecutive stations at Upper Rhone River Basin, a relatively large basin in the Alpine region of Switzerland, daily precipitation at one station, and the twice a week suspended sediment concentration records at the most downstream station between January 1981 and October 2020 are used. Initially, the base flow and the direct runoff associated with streamflow time series are obtained using the sliding interval method. Elasticity analyses between streamflow and suspended sediment concentration together with correlation, autocorrelation, partial autocorrelation, stationarity, and homogeneity are examined by the Augmented Dickey-Fuller and Pettitt's tests, respectively. Then, various stochastic scenarios are generated using the autoregressive moving average exogenous method (ARMAX). It is concluded that the precipitation and direct runoff have fewer effects on the suspended sediment concentration at downstream of the river. Hence, the cumulative effect of the glacier or snowmelt and channel erosion may exceed the effect of rain blown washouts on the suspended sediment concentration at the Port du Scex station. It is found that the ARMAX model results are satisfactory and can be suggested for further application.
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    Urmia lake water depth modeling using extreme learning machine-improved grey wolf optimizer hybrid algorithm
    (Springer, 2021) Sales, Ali Kozekalani; Gul, Enes; Safari, Mir Jafar Sadegh; Ghodrat Gharehbagh, Hadi; Vaheddoost, Babak
    Lake water level changes are relatively sensitive to the climate-born events that rely on numerous phenomena, e.g., surface soil type, adjacent groundwater discharge, and hydrogeological situations. By incorporating the streamflow, groundwater, evaporation, and precipitation parameters into the models, Urmia lake water depth is simulated in the current study. For this, 40 years of streamflow and groundwater recorded data, respectively collected from 18 and 9 stations, are utilized together with evaporation and precipitation data from 7 meteorological stations. Extreme learning machine (ELM) is hybridized with four different optimizers, namely artificial bee colony (ABC), ant colony optimization for continuous domains (ACOR), whale optimization algorithm (WOA), and improved grey wolf optimizer (IGWO). In the analysis, 13 various scenarios with multiple input combinations are used to train and test the employed models. The best scenarios are then opted based on the performance metrics which are applied to assess the accuracy of the methods. According to the results, the hybrid ELM-IGWO shows better performance compared to the ELM-ABC, ELM-ACOR, and ELM-WOA approaches. Results indicate that the groundwater and persistence of the lake water depth have effective roles in models while incorporating higher number of variables can lower the performance of the models. Statistical analysis showed a 62% improvement in the performance of ELM-IGWO in comparison to the ELM-WOA with regard to the root mean square error. The promising outcomes obtained in this study may encourage the application of the utilized algorithms for modeling alternative hydrological problems.