جستجوی مقالات مرتبط با کلیدواژه "الگوریتم چرخه آب" در نشریات گروه "آب و خاک"

Accurate prediction of pore water pressure in the body of earth dams during construction is one of the most important factors in managing the stability of earth dams. In this study, using three different evolutionary neural network models including multilayer perceptron neural network with genetic algorithm, particle swarm optimization and Imperialist Competitive algorithm for estimating the pore water pressure in the body of Kabudwal earth dam at the time of construction, has been studied. Five features including fill level, construction time, reservoir level, impounding rate and fill speed during the 4-year statistical period were selected as the input of the hybrid model in piezometer EP19.7. The composition of the inputs was obtained using the feature selection method and the hybrid water cycle algorithm -artificial neural network. By performing hybrid algorithm and sensitivity analysis and feature selection method, fill level, construction time, reservoir level and dewatering speed were selected as the top four inputs, because the combination of these four features with MSE value of 1.1587 had the least error. In this study, artificial neural network weights are optimized to increase efficiency using the above three meta-heuristic algorithms. In general, according to statistical indicators, the results indicate acceptable accuracy of all three hybrid models. In terms of priority, the ANN-GA hybrid model with the highest accuracy and minimum error and values of , RMSE and MAE are equal to 0.9773, 0.0457 and 0.0399, respectively, is first priority and ANN-PSO and ANN-ICA hybrid models were given the next priorities, respectively.

Given the fact that hydropower energy is the third largest source of electricity generation and also the most important renewable energy producer in the world, the optimal use of the huge and expensive source of water is essential. In this study, water cycle metaheuristic algorithm was used for optimization of the hydropower operation of Jiroft reservoir located in Halilrood basin (south of Iran) for a period of 223 months (from October 2000 to April 2019). The decision variables in the hydropower energy optimization model of the reservoir are the optimal release values of the monthly hydropower output from the reservoirs. After verifying the reliability of the WCA algorithm using several standard benchmark functions, a model was developed for optimal hydropower operation of the Jiroft reservoir. Also, the results of the algorithm were compared with the results of known metaheuristic algorithms of the Harmony search Algorithm (HS) and Imperialist Competitive Algorithm (ICA). The WCA, ICA and HS algorithms were capable to generate energy 7203, 273.66, and 7115.78 MW during the statistical period, respectively. Also, the objective function value for WCA, ICA and HS algorithms, was obtained 4.78, 5.82, and 8.63, respectively. The results revealed that the MSA algorithm was the superior algorithm in optimal hydropower operation.

In this study, the ability of water cycle algorithm in combination with ANN to model the volume of water delivered to the Irrigation and drainage network and determine the effective characteristics, was investigated. For the input of artificial intelligence models, five Features include the volume of water delivered to Sefidrood Irrigation and drainage network for one lags, flow and inlet volume for seven lags, level and reservoir volume for ten lags, were chosen. Using a hybrid algorithm, feature selection method and sensitivity analysis, a triple combination of features (with MSE of 0.00045) is the best input combination. The volume of water delivered to Sefidrood Irrigation and drainage network for one lags according to sensitivity analysis has been the most effective feature in its modeling. Then the artificial neural network weights were optimized to increase efficiency and with the help of two meta-innovative algorithms: water cycle without evaporation (WCA) and water cycle with evaporation (ER.WCA). The ANN-ER.WCA hybrid model with the highest accuracy (with values of R, NRMSE, MAE and NS equal to 0.9915, 0.0975, 0.0090 and 0.9829 in the test period, respectively) is in the first priority and the ANN-WCA and ANN models are next in line, respectively.

Due to water use increasing, attention to optimal water resources allocation is needed. In recent decades, the use of intelligent evolutionary methods for optimization of water allocation was focused more by researchers. The aim of this study is to development on water resources planning model that determined the proper cultivation, optimal exploitation of groundwater and surface water resources although water allocation among crops is a way to minimize the adverse effects of dehydration and increase its revenue. In this study, for maximizing profits, estimating crop water requirements at different periods to optimize the management of cropping patterns and irrigation management in cultivation in Varamin irrigation network using a new evolutionary algorithm was called the water cycle. Then for validation of this method is that a new approach and ensure the integrity of its performance Its results are compared with a genetic algorithm model and linear programming as our base (R2=0.9963). The results showed that the area cropping pattern was not optimal and the area under cultivation of crops such as wheat, barley, tomatoes, Bamjan, melon, alfalfa reaches zero and the new paradigm of the largest area under cultivation to industrial goods and then was assigned cucumbers. While our revenues have increased about 11 percent. In addition to amount of water in different months remain in the network that can be used for many that such as injection into underground aquifers or other crops based on the amount of water available.

In this research, a metaheuristic algorithm, called Water Cycle Algorithm (WCA), was developed in MATLAB software, with the purpose of optimal allocation strategies of a multi-reservoirs system (Golestan and Voshmgir dams) located at Gorganrood basin (north of Iran), for a five year period (from 2007-2008 to 2011-2012). At the first step, the performance of the developed algorithm was successfully assessed through several benchmark functions. Next, it was applied to the monthly allocation of Gorganrood multi-reservoirs system. The objective function was defined as the minimizing of the total deficit for the study period. The results of all applied algorithms were evaluated by reliability and vulnerability criteria. The results of WCA were compared with other developed evolutionary algorithms including Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The WCA, GA and PSO were capable to supply 97.73, 87.07 and 94.3 percent of Golestan dam water demand, respectively. For the Voshmgir dam, the mentioned models could supply 97.06, 87.59 and 94.47 percent of water demand, in same order. The temporal reliability (α=0.9) for WCA, GA and PSO models, was obtained 95, 26.67 and 58.33 percent for Golestan dam and 91.67, 38.33 and 66.67 percent for Voshmgir dam, respectively, revealed that the WCA was superior in optimal allocation of multi-reservoirs system.