intelligent algorithms

By the development of technology and the advancement of technology, many intelligent methods have emerged for estimating the discharge coefficient of different hydraulic structures. One of the structures used in power plants is bottom intake structure. The task of this structure is to transfer the flow to the collecting channel. The advantages of these structures are their stability against dynamic and static loads due to their low level alignment. In the present study, four intelligent algorithms capable of extreme learning machine (ELM), general regression neural networks (GRNN), multivariate adaptive regression spline (MARS) and M5 tree model have been evaluated in modeling of discharge coefficient of bottom intake. The modeling results showed that the ELM algorithm is more accurate than the other algorithms in both training (70% of data) and test (30% of data) periods. In addition, R2 coefficient for the mentioned algorithm was up to 3.74% higher than the other algorithms used. Also the DDR criterion and modeling error histogram showed the superiority of the ELM algorithm. Finally, the computational speed of the algorithms used was compared, which ELM algorithm was 2.557 times faster than the other algorithms. Therefore, the ELM algorithm has high potential for modeling the discharge coefficient in overflows due to its good accuracy and high speed.

Evaporation from the surface of water reservoirs leads to loss of water used in agriculture, industry and drinking. One of the methods of controlling evaporation is the use of physical coatings. In this research, polystyrene sheets, pieces of wood, and honey-bee wax sheets were used as physical coatings on the surface of the water to reduce evaporation from standard buried Colorado evaporation pans. The effect of each coating with three replicates (in three separate pans) was investigated and also three pans were considered as control. Evaporation was measured daily from all pans for 4 months. Results of the experiment showed that polystyrene, wood and wax coatings reduced evaporation by 54, 19 and 18%, respectively. Next, to estimate the percentage of evaporation reduction, decision tree M5, least squared support vector machine (LSSVM) and artificial neural network (ANN) were used. Meteorological data of Semnan synoptic station was used as input data for all the three models. All three methods were highly accurate in the training period. During the test period, the LSSVM method, decision tree M5 and ANN were more accurate, respectively. The values ​​of R2, RMSE and MAE of the LSSVM method at the test step were 0.9834, 2.3718, and 1.7212, respectively. The general results of this study showed that using polystyrene coating can have proper function in controlling evaporation from reservoirs and water storage pools.