The thirst of the Earth for energy is lurching towards catastrophe in an era of increasing water shortage where most of the power plants are hydroelectric. The hydro-based power systems are facing challenges in determining day-ahead generation schedules of cascaded hydropower plants. The objective of the current study is to find a speedy and practical method for predicting and classifying the future schedules of hydropower plants in order to increase the overall efficiency of energy by utilizing the water of cascaded hydropower plants. This study is significant for water resource planners in the planning and management of reservoirs for generating energy. The proposed method consists of data mining techniques and approaches. The energy production relationship is first determined for upstream and downstream hydropower plants by using multiple linear regression. Then, a cluster analysis is used to find typical generation curves with the help of historical data. The decision tree algorithm C4.5, Iterative Dichotomiser 3-IV, improved C4.5 and Chi-Squared Automatic Interaction Detection are adopted to quickly predict generation schedules, and detailed comparison among different algorithms are made. The decision tree algorithms are solved using SIPINA software. Results show that the C4.5 algorithm is more feasible for rapidly generating the schedules of cascaded hydropower plants. This decision tree algorithm is helpful for the researchers to make fast decisions in order to enhance the energy production of cascaded hydropower plants. The major elements of this paper are challenges and solution of head sensitive hydropower plants, using the decision-making algorithms for producing the generation schedules, and comparing the generation from the proposed method with actual energy production.

The real time hydrological data may contain noise, missing information and deviation from its original scale due to complex and nonlinear nature of hydrological processes. The data when used as it is in hydrological forecasting may create uncertainty in hydrological models, especially in data-driven models which fully rely upon the input-output data. The current research provides a simple preprocessing approach to improve the performance of ANN-based streamflow estimation models through providing a better input state. The two-step preprocessing approach includes; the data transformation through a family of power transformation, the Box-Cox transformation, and the selection of appropriate input variables through the Gamma Test. The original data, which is essentially antecedent upland catchment information of thirteen stations located in Upper Indus Basin (UIB), comprises of twenty inputs, including precipitation, solar radiation and discharge. The Box-Cox transformation has been applied to prepare a transformed data-set and the power factor, λ, (with best value of 0.005), for this transformation, has been determined using probability plots and histogram characteristics. Input combination selection procedure is carried out in WinGamma environment with the help of Genetic Algorithm (GA). Two-layer ANN models have been trained through Broyden, Fletcher and Goldfrab Shano (BFGS) training algorithm for both original and transformed data-sets. The comparison of models clearly indicate that the models developed through transformed data-set showed better performance in both training and testing phases with high values of NSE and R² which is above 90% in most of the cases, and less other statistical errors including RMSE, VARIANCE and BIAS. Simple preprocessing options, could significantly reduce the uncertainty in ANN based hydrological models through improving the quality of real time hydrological data.

The hydraulic performance of stilling basins is affected by their size and geometry, which can be predicted by local scour. In 2008, based on a rigid bed study, the stilling basin of Taunsa barrage was remodeled, in which the old friction and baffle blocks were replaced with chute blocks and end sills. However, the study did not consider the effects of the remodeled basin on the erodible bed and only investigated hydraulic jumps. Therefore, this study developed FLOW-3D scour models for a designed flow of 24.28 m³/s/m to investigate the flow field and local scouring downstream of old and remodeled basins. The results showed that as compared to Large Eddy Simulation (LES) and Standard K-ε models, the Renormalization Group (RNG-K-ε) model predicted the scour profiles with better accuracy, for which the coefficient of determination (R²) reached 0.736, 0.823, and 0.747 for bays 33, 34, and 55, respectively. Downstream of the remodeled basin, the net change in sediment bed was 88%, 91%, and 95% in the LES, Standard, and RNG-K-ε models, respectively. However, downstream of the old basin, the net change in sediment bed reached 51%. Conclusively, based on the results, the study suggests investigating scour downstream of Taunsa Barrage using other discharges and sediment transport rate equations.

The present study investigated the performance of three different stilling basins, i.e., modified United State Bureau of Reclamation (USBR) Type III, USBR Type II, and wedge-shaped baffle blocks (WSBB), using FLOW-3D scour models. Field data of the riverbed profile are employed to validate the present models. After comparison, the results of statistical indices, i.e., coefficient of determination (R²) and Nash–Sutcliffe model efficiency coefficient (NSE), indicated that the Renormalization Group (RNG-K-ϵ) showed good agreement with the field data, with R2 and NSE values of 0.9094 and 0.896, respectively. Validated models are used to simulate velocity field and local bed shear stress (BSS) and scour for design and flood discharges of 28.30 m³/s/m and 17.5 m³/s/m, respectively. At 28.30 m³/s/m, the results indicated that the riverbed downstream of the remodeled basin was completely exposed, while, at 17.5 m³/s/m, the net change in bed reached 85%. At 28.30 m³/s/m, the net change at the centerline of models reached 51% and 67% in USBR Type III and WSBB basins, respectively. At 17.5 m³/s/m, compared to Type II and III basins, the WSBB basin indicated less BSS, which significantly reduced the scour. Conclusively, the Type II basin showed less energy dissipation for the studied flows, while the WSBB basin improved flow fields downstream of the barrage.

Hydraulic structures, such as barrages, play an important role in the sustainable development of several regions worldwide. The aim of this novel study is to identify the critical hydraulic parameters (CHPs) of Taunsa Barrage, built on the Indus River. These CHPs, including free surface profiles, flow depths, Froude number, velocity profiles, energy dissipation and turbulence kinetic energy, were investigated using simulation via FLOW-3D numerical models. Incompressible Reynolds-averaged Navier–Stokes (RANS) equations on each computational cell were solved using the numerical methods available in FLOW-3D. The simulation results indicated that the locations of hydraulic jumps (HJs) were lower than that were reported in the previous one-dimensional study. Similarly, the distances of the HJs from the downstream toe of the glacis were reached at 2.97 m and 6 m at 129.10 m and 130.30 m tailwater levels, respectively, which deviated from the previous studies. In higher tailwater, the sequent depth ratio also deviated from the previous data. The maximum turbulent kinetic energies were observed in the developing regions of HJs, which were found to be decreased as the distance from the HJ was increased. The results of this research will be highly useful for engineers working in the field of design of hydraulic structures.