Evaporation from reservoirs is an important issue frequently occurring in dry, hot regions like Iran. Since the laboratory and field studies of evaporation control are difficult, time-consuming, and costly, and the investigation of various possible modes is not possible, numerical models with high capabilities are widely used to analyze the hydrological processes. This article aimed to investigate the effect of windbreaks on reducing evaporation of lakes and reservoirs in dry areas and determine the most optimal location and layout of windbreaks using the FLUENT model. Initial investigations showed that wind is the most important factor of evaporation in the Chahnimeh Region of Sistan, Iran. The results showed that if solid windbreaks with 25% casement (height of 2 and distance of 66 m) are vertically installed in a northwesterly direction, evaporation can be effectively reduced. Although the use of wind breaks in Chahnimeh can help significantly reduce evaporation, it cannot be fully controlled. That is why diagonal windbreaks with 30, 45, and 60º were designed to integrate the windbreaks with other evaporation control methods such as solar panels. The results showed that 60º had the greatest amount of evaporation reduction and were integrated with other methods to control evaporation.
The longitudinal dispersion coefficient (LDC) of river pollutants is considered as one of the prominent water quality parameters. In this regard, numerous research studies have been conducted in recent years, and various equations have been extracted based on hydrodynamic and geometric elements. LDC’s estimated values obtained using different equations reveal a significant uncertainty due to this phenomenon’s complexity. In the present study, the crow search algorithm (CSA) is applied to increase the equation’s precision by employing evolutionary polynomial regression (EPR) to model an extensive amount of geometrical and hydraulic data. The results indicate that the CSA improves the performance of EPR in terms of R2 (0.8), Willmott’s index of agreement (0.93), Nash–Sutcliffe efficiency (0.77), and overall index (0.84). In addition, the reliability analysis of the proposed equation (i.e., CSA) reduced the failure probability (Pf) when the value of the failure state containing 50 to 600 m2/s is increasing for the Pf determination using the Monte Carlo simulation. The best-fitted function for correct failure probability prediction was the power with R2 = 0.98 compared with linear and exponential functions.
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