Photovoltaic power plant outputs depend on the daily global solar radiation (DGSR). The main issue with DGSR data is its lack of precision. The potential unavailability of DGSR data for several sites can be attributed to the high cost of measuring instruments and the intermittent nature of time series data due to equipment malfunctions. Therefore, DGSR prediction research is crucial nowadays to produce photovoltaic power. Different artificial neural network (ANN) models will give different DGSR predictions with varying levels of accuracy, so it is essential to compare the different ANN model inputs with various sets of meteorological stochastic variables. In this study, radial basis function neural network (RBFNN), long short-term memory neural network (LSTMNN), modular neural network (MNN), and transformer model (TM) are developed to investigate the performances of these algorithms for the DGSR prediction using different combinations of meteorological stochastic variables. These models employ five stochastic variables: wind speed, relative humidity, minimum, maximum, and average temperatures. The mean absolute relative error for the transformer model with input variables as average, maximum, and minimum temperatures is 1.98. ANN models outperform traditional models in predictive accuracy.

Dissolved oxygen (DO) is an important parameter in assessing water quality. The reduction in DO concentration is the result of eutrophication, which degrades the quality of water. Aeration is the best way to enhance the DO concentration. In the current study, the aeration efficiency (E20) of various numbers of circular jets in an open channel was experimentally investigated for different channel angle of inclination (θ), discharge (Q), number of jets (Jn), Froude number (Fr), and hydraulic radius of each jet (HRJn). The statistical results show that jets from 8 to 64 significantly provide aeration in the open channel. The aeration efficiency and input parameters are modelled into a linear relationship. Additionally, utilizing WEKA software, three soft computing models for predicting aeration efficiency were created with Artificial Neural Network (ANN), M5P, and Random Forest (RF). Performance evaluation results and box plot have shown that ANN is the outperforming model with correlation coefficient (CC) = 0.9823, mean absolute error (MAE) = 0.0098, and root mean square error (RMSE) = 0.0123 during the testing stage. In order to assess the influence of different input factors on the E20 of jets, a sensitivity analysis was conducted using the most effective model, i.e., ANN. The sensitivity analysis results indicate that the angle of inclination is the most influential input variable in predicting E20, followed by discharge and the number of jets.

The primary issues in the Himalayan Rivers are sediment and cavitation degradation of the hydroelectric power turbine components. During the monsoon season, heavy material is transported by streams in hilly areas like the Himalayas through regular rainfalls, glacial and sub-glacial hydrological activity, and other factors. The severe erosion of hydraulic turbines caused by silt abrasion in these areas requires hydropower facilities to be regularly shut down for maintenance, affecting the plant's overall efficiency. This article provides an in-depth examination of the challenges that can lead to cavitation, silt erosion, and a decrease in the efficiency of various hydroelectric turbines, and it demands attention on the design, manufacture, operation, and maintenance of the turbines. This study's main objective is to critically evaluate earlier theoretical, experimental, and numerical evaluation-based studies (on cavitation and silt erosion) that are provided and addressed throughout the study. As a part of this study, various strategies for mitigating the effects of these problems and elongating the time that turbine may be utilized before they must be replaced have been provided.