Irrigation Management Based on Reservoir Operation with an Improved Weed Algorithm

Abstract

Water scarcity is a serious problem throughout the world. One critical part of this problem is supplying sufficient water to meet irrigation demands for agricultural production. The present study introduced an improved weed algorithm for reservoir operation with the aim of decreasing irrigation deficits. The Aswan High Dam, one of the most important dams in Egypt, was selected for this study to supply irrigation demands. The improved weed algorithm (IWA) had developed local search ability so that the exploration ability for the IWA increased and it could escape from local optima. Three inflows (low, medium and high) to the reservoir were considered for the downstream demands. For example, the average solution for the IWA at high inflow was 0.985 while it was 1.037, 1.040, 1.115 and 1.121 for the weed algorithm (WA), bat algorithm (BA), improved particle swarm optimization algorithm (IPSOA) and genetic algorithm (GA). This meant that the IWA decreased the objective function for high inflow by 5.01%, 5.20%, 11.65% and 12% compared to the WA, BA, IPSOA and GA, respectively. The computational time for the IWA at high inflow was 22 s, which was 12%, 18%, 24% and 29% lower than the WA, BA, IPSOA and GA, respectively. Results indicated that the IWA could meet the demands at all three inflows. The reliability index for the IWA for the three inflows was greater than the WA, BA, IPSOA and GA, meaning that the released water based on IWA could well supply the downstream demands. Thus, the improved weed algorithm is suggested for solving complex problems in water resources management.

Precipitation Forecasting Using Multilayer Neural Network and Support Vector Machine Optimization Based on Flow Regime Algorithm Taking into Account Uncertainties of Soft Computing Models

“…Therefore, in all scenarios, the SLP-FLA model is closer to the reference point than the other two models. On the other hand, in Scenario 3, all models perform better than other scenarios [18][19][20][21]. Figure 5a illustrated the maximum relative error that have been achieved from different models.…”

“…This splitting percentage was selected upon the strong recommendations from previous studies. Therefore, the current setup for this model, 70% for training and 30% for testing, is suitable for this study [18][19][20][21]. The data were obtained from rain gauge stations that operated by the meteorological department in Iran.…”

“…The lead times were suggested after initial screening of correlation between lag times and precipitation values to be one-month-ahead forecasting. The precipitation and the temperature values are selected to be the input variables for the model because the high correlation between these two variables has been proved, and, hence, it is necessary while developing the forecasting model for precipitation to consider the temperature as one of the input variables [18][19][20][21].…”

“…The result showed that the neural network model and the outputs had a high correlation with the precipitation model of Western Australia and a higher NSE coefficient than the multiple regression model. In any case, previous studies have shown that methods such as the neural network, fuzzy-neural network and support vector machine have good capabilities to forecast precipitation [19][20][21][22][23][24]. Models such as the neural network and support vector machine have a good performance, however, it is important to note that they have unknown parameters, the values of which should be obtained before precipitation forecast [25][26][27].…”

“…where Y obs i is observed data, Y sim i is simulated data, Y mean is the mean data, MAE is the mean absolute error of data, RSR is the root mean square error-observations standard deviation ratio, and NSE is the Nash-Sutcliffe efficiency coefficient. The above indexes are widely used to evaluate the performances of the soft computing models [15][16][17][18][19][20][21]30].…”