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Showing 26 results for Optimization
Development of Hydraulic-Economic Simulation-Optimization Model for the Design of Basin Irrigation
M. Akbari,
Volume 24, Issue 4 (2-2021)
Abstract
The objective of this research was the development of a hydraulic-economic simulation-optimization model for the design of basin irrigation. This model performed hydraulic simulation (design of basin irrigation), using Volume Balance model, economic simulation through calculating sum of four seasonal costs and optimization using NSGAII multi-objective meta-heuristic algorithm. For programming, MATLAB programming software was applied. The optimizations of functional, multi-dimensional, static, constraint, continuous, multi-objective and meta-heuristic were applied for the optimization of the objective functions. Decision variables selected from simulation inputs were calculated in such a way that the  hydraulic objective function (minimizing linear combination of seven performance indicators) and economic objective function (total seasonal cost based on sum of water cost, labor cost, basin preparing cost and channel drilling cost) were minimized. Data of one the experimental field was used for the purpose of simulation. After initial simulation, optimization of the experimental field was done using NSGAII multi-objective meta-heuristic algorithm with tuned parameters. Optimization using the suggested model shoed the decrease (improvement) of objective functions rather than initial simulation performance. As a result, the suggested model could be regarded as is a specialized tool for basin irrigation, showing a good performance, despite its simplicity.

Optimization of ANFIS Model using Genetic Algorithm for Estimation of Scour Depth around Bridge Abutments
E. Yarmohammadi, S. Shabanlou, A. Rajabi,
Volume 25, Issue 1 (5-2021)
Abstract
Optimization of artificial intelligence (AI) models is a significant issue because it enhances the performance and flexibility of the numerical models. In this study, scour depth around bridge abutments with different shapes was estimated by means of ANFIS and ANFIS-Genetic Algorithm. In other words, the membership functions of the ANFIS model were optimized using the genetic algorithm, finding that the performance of ANFIS model was increased. Firstly, effective input parameters on the scour depth around bridge abutments were defined. Then, by using the input parameters, eleven ANFIS and ANFIS-GA models were produced. Next, the superior ANFIS and ANFIS-GA models were introduced by analyzing the numerical results. For example, the correlation coefficient and scatter index for ANFIS model were calculated to be 0.979 and 0.070; for ANFIS-GA, these were 0.986 and 0.056, respectively. In addition, the average discrepancy ratio (DRave) for ANFIS and ANFIS-GA models was 0.984 and 0.988, respectively. Also, it was shown that the ANFIS-GA models had more accuracy, as compared to the ANFIS models. Moreover, a sensitivity analysis showed that Froude number (Fr) and ratio of flow depth to radius of scour hole (h/L) were the most influential input parameters for simulating the scour depth around bridge abutments.

Two-Objective Simulation-Optimization Model for Cropping Pattern and Water Allocation in Semnan Plain
M. Ghodspour, M. Sarai Tabrizi, A. Saremi, H. Kardan Moghadam, M. Akbari,
Volume 25, Issue 3 (12-2021)
Abstract
The application of simulation-optimization models is a valuable tool for selecting the appropriate cropping pattern. The main objective of this research is to develop a two-objective simulation-optimization model to determine the pattern of cultivation and water allocation. The model performs the optimization with the multi-objective metamorphic algorithm (MOALO) after simulating different states of the cultivation pattern. The decision variables including land and water allocated to ten-day periods of plant growth were designed in a way that the minimum utilization of water resources and economic maximization were identified as target functions. The developed model was used to simulate and optimize the cultivation pattern with an area of ​​5500 hectares and water allocation of Semnan plain with renewable water at the rate of 60.8 million cubic meters. Harvesting scenarios of 80 (GW80) and 100 (GW100) percent of renewable groundwater and scenarios of change in existing cropping pattern of 30 (AC30) and 60 (AC60) percent were considered and each scenario was simulated with the MOALO algorithm. Optimization using the proposed model in four scenarios improved the water and economic objective functions compared to the initial simulation performance. The results showed that the four proposed scenarios were obtained by minimizing the water objective function and maximizing the economic objective function relative to the current situation (simulation). In general, the proposed model had a good performance despite its simplicity, which is a specialized tool to optimize the crop pattern with water allocation.

Estimation of Actual Evapotranspiration, Water productivity, and Irrigation Efficiency of Wheat Fields in Surface and Sprinkler Irrigation Systems Using Remote Sensing
F. Ghasemi-Saadat Abadi, S. Zand-Parsa, M. Mahbod,
Volume 25, Issue 4 (3-2022)
Abstract
In arid and semi-arid regions, water resource management and optimization of applying irrigation water are particularly important. For optimization of applying irrigation water, the estimated values of actual evapotranspiration are necessary for avoiding excessive or inadequate applying water. The estimation of actual crop evapotranspiration is not possible in large areas using the traditional methods. Hence, it is recommended to use remote sensing algorithms for these areas. In this research, actual evapotranspiration of wheat fields was estimated using METRIC algorithm (Mapping EvapoTranspiration at high Resolution with Internalized Calibration), using ground-based meteorological data and satellite images of Landsat8 at the Faculty of Agriculture, Shiraz University, in 2016-2018. In the process of METRIC execution, cold pixels are located in well-irrigated wheat fields where there is no water stress and maximum crop evapotranspiration occurred. The estimated maximum values of evapotranspiration using the METRIC algorithm were validated favorably using the obtained values by the AquaCrop model with NRMSE (Normalized Root Mean Square Errors) equal to 0.12. Finally, the values of water productivity (grain yield per unit volume of evapotranspiration) and irrigation efficiency were estimated using the values of predicted actual evapotranspiration using remote sensing technique. The values of measured irrigation water and produced wheat grain yield in 179 ha were estimated at 0.86 kg m-3 and 75%, respectively.

Maximizing Economic Water Productivity Index in Agriculture in Different Levels of Irrigation
A. Mehrabi, M. Heidar Pour, H. R. Safavi,
Volume 25, Issue 4 (3-2022)
Abstract
Designing an optimal crop pattern and on-time water allocation of water resources along with deficit irrigation are among the optimal solutions to maximize the water economic efficiency index. In this paper, the simultaneous optimization of crop pattern and water allocation are discussed using the deficit irrigation method. The study area is located west of the Qazvin plain irrigation network. The six different levels of percentage reduction of irrigation rate (0, 0 to 10, 0 to 20, 0 to 30, 0 to 40, and 0 to 50%) in three climatic conditions consist of dry, normal, and wet years were compared. The best irrigation scenario was selected for each year, and the results were compared with the existing crop pattern of the same year. The new crop pattern included the main crops of the region and the addition of rapeseed. The objective was to reach the maximum net benefit per unit volume of water by considering the maximum extraction of monthly and annual surface and groundwater. The results showed that the best scenario in the dry year was maximum deficit irrigation up to 20%, in a normal year full irrigation, and a wet year maximum deficit irrigation up to 10%. The improvement of economic water productivity in a dry year was 52.2%, in a normal year 41.5%, and in a wet year is 19.6% compared to the existing crop pattern. The average percentage of annual irrigation supply increases from 64.3 to 91.7% in a dry year, from 70 to 100% in a normal year, and from 77.5 to 97.1% in a wet year. Also, the relative yield of all crops, especially wheat, alfalfa, and sugar beet significantly increases. Therefore, the gravitational search algorithm as an optimization model can be considered in selecting the suitable crop pattern and allocation of surface and groundwater resources concerning economic benefits in irrigation networks management.

Environmental Optimization of the Cultivated Area of Shahid Chamran Irrigation Network Using System Dynamics Approach
S. Azadi, H. Nozari, S. Marofi, B. Ghanbarian,
Volume 27, Issue 3 (12-2023)
Abstract
One of the strategies for agricultural development is the optimal use of irrigation and drainage networks, which will lead to higher productivity and environmental protection. The present study used the system dynamics approach to develop a model for simulating the cultivated area of the Shahid Chamran irrigation and drainage network located in Khuzestan province by considering environmental issues. Limit test and sensitivity analysis were used for model validation. The results showed the proper performance of the model and the logical relationship between its parameters. Also, the cropping pattern of the network was determined in two modes of non-stepwise and stepwise changes to determine the optimal cultivated area of the Shahid Chamran network with environmental objectives and minimize the amount of salt from drains. The results showed that the amount of optimized output salt from the network has decreased in both non-stepwise and stepwise changes compared to the existing situation in the region. The total output salt in the current situation, from 2013 to 2017, was obtained at 2799, 2649, 2749, 2298, and 2004 tons.day-1, respectively, in the stepwise changes, are 2739, 2546, 2644, 2223, and 1952 tons.day-1, and finally, in the non-stepwise changes, are 2363, 2309, 2481, 2151, and 1912 tons.day-1. The results showed that the non-stepwise changes due to considered limitations have been more successful in reducing output salt than the stepwise changes. The analysis of the results showed the model's success in optimizing and achieving the desired goals. The results showed that the present model has good accuracy in simulating and optimizing the irrigation network, cropping pattern, and defining other scenarios.

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