Solar energy is a major type of renewable energy, and its estimation is important for decision-makers. This study introduces a new prediction model for solar radiation based on support vector regression (SVR) and the improved particle swarm optimization (IPSO) algorithm. The new version of algorithm attempts to enhance the global search ability for the PSO. In practice, the SVR method has a few parameters that should be determined through a trial-and-error procedure while developing the prediction model. This procedure usually leads to non-optimal choices for these parameters and, hence, poor prediction accuracy. Therefore, there is a need to integrate the SVR model with an optimization algorithm to achieve optimal choices for these parameters. Thus, the IPSO algorithm, as an optimizer is integrated with SVR to obtain optimal values for the SVR parameters. To examine the proposed model, two solar radiation stations, Adana, Antakya and Konya, in Turkey, are considered for this study. In addition, different models have been tested for this prediction, namely, the M5 tree model (M5T), genetic programming (GP), SVR integrated with four different optimization algorithms SVR-PSO, SVR-IPSO, Genetic Algorithm (SVR-GA), FireFly Algorithm (SVR-FFA) and the multivariate adaptive regression (MARS) model. The sensitivity analysis is performed to achieve the highest accuracy level of the prediction by choosing different input parameters. Several performance measuring indices have been considered to examine the efficiency of all the prediction methods. The results show that SVR-IPSO outperformed M5T and MARS.

Evaporation from surface water plays a crucial role in water accounting of basins, water resource management, and irrigation systems management. As such, the simulation of evaporation with high accuracy is very important. In this study, two methods for simulating pan evaporation under different climatic conditions in Iran were developed. In the first method, six experimental relationships (linear, quadratic, and cubic, with two input combinations) were determined for Iran’s six climate types, inspired by a multilayer perceptron neural network (MLP-NN) neuron and optimized with the genetic algorithm. The best relationship of the six was selected for each climate type, and the results were presented in a three-dimensional graph. The best overall relationship obtained in the first method was used as the basic relationship in the second method, and climatic correction coefficients were determined for other climate types using the genetic algorithm optimization model. Finally, the accuracy of the two methods was validated using data from 32 synoptic weather stations throughout Iran. For the first method, error tolerance diagrams and statistical coefficients showed that a quadratic experimental relationship performed best under all climatic conditions. To simplify the method, two graphs were created based on the quadratic relationship for the different climate types, with the axes of the graphs showing relative humidity and temperature, and with pan evaporation, were drawn as contours. For the second method, the quadratic relationship for semi-dry conditions was selected as the basic relationship. The estimated climatic correction coefficients for other climate types lay between 0.8 and 1 for dry, semi-dry, semi-humid, Mediterranean climates, and between 0.4 and 0.6 for humid and very humid climates, indicating that one single relationship cannot be used to simulate pan evaporation for all climatic conditions in Iran. The validation results confirmed the accuracy of the two methods in simulating pan evaporation under different climatic conditions in Iran.

This study is the first study that worked on the temporal and spatial distributions of annual rainfall (Pyear) and maximum 24-h rainfall (Pmax24h) in the Semnan province. For this purpose, different statistical distributions were used to estimate the temporal Pyear and Pmax24h in the Semnan province. Six synoptic stations across the province were studied and all stations had complete Pyear and Pmax24h data. Different return periods were studied. The goodness fit test of statistical distributions for Pyear showed that about 67% of the stations follow the Generalized Pareto (GP) distribution. Considering the Pmax24h, 50% of the stations follow the GP distribution, and for the ratio of Pmax24h to Pyear, 50% of stations follow the Generalized Extreme Value (GEV) distribution. The spatial distribution of Pyear and Pmax24h showed that in all return periods, by moving to the southeast of the province, precipitation amounts decreased. While moving toward the Shahmirzad station, the amounts of Pyear and Pmax24h increased. Also, there was a logical relationship between the Pyear and Pmax24h. Consequently, the minimum value and the maximum value of the R2 coefficient in different return periods were equal to 0.992 and 0.980, respectively.

The present study aims to evaluate the performance of the impervious surface as a control (O), sandy loam substrate (SL), gravel (G), gravel with geocell layer (GGE), rosemary (R), rosemary with geocell layer (RRE), turf (T), and turf with geocell layer (TGE) in the reduction of runoff volume, time-to-start runoff (TR), runoff coefficient (C), time-to end runoff (TER), peak flow rate (PF), time to peak (TP), and time base (TB) in the laboratory dimension under three different scenarios of rainfall intensity and two different slopes using a rainfall simulator. The results revealed a significant difference between the data at the level of 5% in all cases. Generally, three rainfall scenarios for all hydrological parameters TR, TER, TP, TB, C, and PF were classified into different groups. In all cases, GGE treatment performed better than that of the rest of the test groups in reducing runoff and cumulative volume. Further, treatments O and GGE experienced the highest and lowest flow rates, respectively. For a specific scenario of rainfall intensity and slope, the value of C is the lowest for GGE treatment. Finally, the implementation of geocell in the pavements was able to delay the time to start runoff.

In this study, hydraulic jumps over expanding beds with artificial roughness were simulated using FLOW-3D across Froude numbers ranging from 4.34 to 9.37. The simulations were conducted on both smooth and rough beds, with roughness in the form of half-spheres of 3, 4, and 5 cm in diameter, and divergence angles of 7°, 14°, and 90°. The results showed that for maximum discharge in a sudden divergent channel, a rough bed with 5-cm diameter elements reduced flow depth by 19.77% compared to a smooth bed. Additionally, in all scenarios, the ratio of y2/y1 increased as the Froude number increased. In the second phase, soft computing models – such as Linear Regression, Support Vector Regression, Decision Tree, Random Forest, Bagging, Gradient Boosting, MLP, and Stacking – were employed to model the relationships between input parameters (Fr1, θ, D/b1, and Kb) and outputs (y2/y1 and Lj/y1). The R2 coefficient value in the training stage of the Stacking model for the parameter (y2/y1) was 0.978 and in the testing stage it was 0.988, and for the parameter (Lj/y1) in the training and testing stages this coefficient was estimated to be 0.971 and 0.987, respectively.