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.

Nowadays, a major part of the stored surface water resources is wasted by evaporation. By implementing low-cost methods to prevent water evaporation, it is possible to return this part of water to the agricultural, industrial, and drinking cycles. The present study aimed to evaluate the effect of polystyrene (compressed foam), pieces of wood, and synthetic beeswax cover with different surface coverage of 60%, 70%, and 80% in the standard Colorado Sunken evaporation pan on the evaporation rate. The materials used in this research are accessible, natural, and affordable in terms of practical applications. Then, the parameters of minimum and maximum temperature, maximum and minimum relative humidity, sunny hours, pressure, and wind speed were obtained from Semnan Synoptic Station near the test site. Due to limited studies in simulation of evaporation reduction through intelligent methods, the evaporation values of polystyrene, pieces of wood, and synthetic beeswax covers were simulated and estimated by using artificial intelligence methods of M5 model tree, Artificial Neural Network, and Least-Square Support Vector Machine. Results indicated that compressed foam, pieces of wood, and synthetic beeswax covers, each with 60%, 70%, and 80% coverage could reduce the evaporation rate by 43%, 54%, and 65%, 10%, 19%, and 26%, and 8%, 18%, and 25%, respectively. From a statistical point of view, the meteorological data received from Semnan Synoptic Station had significant correlations with evaporation rate from station pan, Colorado pan, and pans containing polystyrene, pieces of wood, and synthetic beeswax covers. The temperature and relative humidity parameters are factors affecting the evaporation rate in Semnan station. After evaluating the simulation accuracy based on the parameters of the root mean square error (RMSE), mean absolute error (MAE), and determination coefficient ( R 2 ), the best estimation was obtained by the LSSVM method with R 2 = 99%. From an economic viewpoint, the compressed polystyrene sheet and synthetic beeswax imposed $1 and 8 cents per m 2 in Iran. Moreover, the discarded pieces of wood that are used in the present study are inexpensive. Regarding the cost and performance of the material in reducing evaporation, it is recommended to use polystyrene material, followed by pieces of wood and beeswax.

Evaporation from surface water plays a key role in water accounting of basins, water resources management, and irrigation systems management, so simulating 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. In the second method, the best overall relationship obtained in the first method was used as the basic relationship, 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 was 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.