The accurate forecast of air temperature plays an important role in water resources management, land–atmosphere interaction, and agriculture. However, it is difficult to accurately predict air temperature due to its non-linear and chaotic nature. Several deep learning techniques have been proposed over the last few decades to forecast air temperature. This study provides a comprehensive review of artificial neural network (ANN)-based approaches (such as recurrent neural network (RNN), long short-term memory (LSTM), etc.), which were used to forecast air temperature. The focus is on the works during 2005–2020. The review shows that the neural network models can be employed as promising tools to forecast air temperature. Although the ANN-based approaches have been utilized widely to predict air temperature due to their fast computing speed and ability to deal with complex problems, no consensus yet exists on the best existing method. Additionally, it is found that the ANN methods are mainly viable for short-term air temperature forecasting. Finally, some future directions and recommendations are presented.

The stilling basin has been accepted to be the most powerful hydraulic structure for the dissipation of the flow energy. The size and geometry of the stilling basin affect the formation of flow patterns, which can be influential for hydraulic performance of the whole system. The Nazloo Dam in Iran was selected as the study area. The USBR II stilling basin was conducted for four convergence angles (5 • , 7.5 • , 10 • , and 12.5 • ). The convergence walls cause the jump to stabilize inside the basin and cause the energy loss to increase in the stilling basin. To simulate the hydraulic jump in the convergence stilling basin conditions in this region, a free surface computational fluid dynamics (CFD) numerical model has been applied. The commercially known software, FLOW-3D , was applied to numerically solve the Navier-Stokes equations for solution domains, namely the shout, the stilling basin and the downstream of dam, and to estimate the turbulence flow, the standard k-ε and RNG models was used. These models are based on the volume-of-fluid method, and they are capable of simulating the hydraulic jump. The calculated results such as the pressure, the velocities, the flow rate, the surface height air entranced, the kinetics energy, the kinetics energy dissipated, and the Froude number were compared with the scale model data where available. This allowed a comparison for the use of CFD as a cost-effective alternative to physical models. The physical model and CFD model results showed good correlations.

This paper was concerned to simulate both wet and dry bed dam break problems. A high-resolution finite volume method (FVM) was employed to solve the one-dimensional (1D) and two-dimensional (2D) shallow water equations (SWEs) using an unstructured Voronoi mesh grid. In this attempt, the robust local Lax-Friedrichs (LLxF) scheme was used for the calculating of the numerical flux at cells interfaces. The model named V-Break was run under the asymmetry partial and circular dam break conditions and then verified by comparing the model outputs with the documented results. Due to a precise agreement between those output and documented results, the V-Break could be considered as a reliable method for dealing with shallow water (SW) and shock problems, especially those having discontinuities. In addition, statistical observations indicated a good conformity between the V-Break and analytical results clearly.

No abstract