Optimal operation of multi-reservoirs by water cycle algorithm

Abstract

Optimal operation of reservoirs is one of the most important issues in water resources management. In this study, a novel metaheuristic optimisation algorithm, called the water cycle algorithm (WCA), was used to derive operating policy for a multi-reservoir system. In the first step, the performance of the model was successfully assessed through several benchmark functions. The WCA was then used to derive the optimal operation of four- and ten-reservoir systems. It was then applied to the monthly operation of Golestan and Voshmgir consecutive dams located in Gorganrood basin in the north of Iran. In this way, the objective function was defined as minimising the total deficit for the study period. The WCA results were compared with the results of other developed evolutionary algorithms, including a genetic algorithm, harmony search algorithm, particle swarm optimisation and imperialist competitive algorithm. The results showed that, for cases of four- and ten-reservoir systems, the best solutions achieved by the WCA were 306·3918 and 1172·4197, which had differences of 0·5% and 1% compared with the global optimum solutions, respectively. In addition, it was found that the WCA was superior to other algorithms in calculating the annual deficit of the Golestan–Voshmgir multi-reservoir system.

Artificial intelligence accuracy assessment in NO2 concentration forecasting of metropolises air

“…In the current study, logarithmic sigmoid and linear activation functions were examined to optimize the network. The back-propagation (BP) training algorithm is found to be the most common and powerful nonlinear statistical technique in MLP networks 29,30 . All computations were developed in MATLAB R2016b software.…”

Application of ANN modeling techniques in the prediction of the diameter of PCL/gelatin nanofibers in environmental and medical studies

“…[21][22][23] An ANN consists of interconnected processing elements, such as an input layer, various hidden layers and an output layer which is capable of learning from samples, using transfer functions between neurons and a specic learning algorithm in the structure of a program without being affected by data noise. [24][25][26] Nowadays, different models and learning algorithms can be applied to modeling and controlling the electrospinning processes. 27,28 In this paper, we have compared the multi-layer perceptron (MLP), radial basis function (RBF), and support vector machine (SVM) models for predicting the diameter of PCL/gelatin nanobers.…”

MLR and ANN Approaches for Prediction of Synthetic/Natural Nanofibers Diameter in the Environmental and Medical Applications

“…The accuracy of the regression models increases by using MLR while it declines when independent variables increase. Nonlinear and dynamic modeling techniques like artificial neural network (ANN) are modeling tools to solve complex cases, quality control, data mining, and linear and nonlinear multivariate regression problems [16][17][18][19] . In recent years ANN approach as one of the most popular artificial intelligence approaches has been used to model the electrospinning technique, mostly aimed at predicting the diameter of nanofibers electrospinning 16,20 .…”