Showing 32 results for Optimization
M. Akbari,
Volume 24, Issue 4 (11-2020)
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.
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.
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.
F. Ghasemi-Saadat Abadi, S. Zand-Parsa, M. Mahbod,
Volume 25, Issue 4 (12-2021)
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.
A. Mehrabi, M. Heidar Pour, H. R. Safavi,
Volume 25, Issue 4 (12-2021)
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.
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.
H. Ebrahimi Golbosi, E. Fazel Najafabadi, M. Shayannejad,
Volume 28, Issue 2 (8-2024)
Abstract
Surface irrigation is one of the most common irrigation methods. Due to the low efficiency of surface irrigation, water loss is significant in this system. It is necessary to know the characteristics and coefficients of water infiltration rate in the soil for accurate and adequate planning of surface irrigation. One of the equations used in this field is Phillip's infiltration equation. In this study, the infiltration coefficients of Phillip's equation and Manning's roughness coefficient in border irrigation are determined based on the comparison of the actual advance curve with the advance curve calculated with the dynamic wave model, and the results were compared with the double cylinder method and the two-point method of Ebrahimian et al. (5). The actual infiltration volume was obtained from the difference between the inlet and outlet volumes. The error of the mentioned method in calculating the infiltration volume was 5.53%. Meanwhile, the errors in the double cylinder and two-point Ebrahimian (5) method were 59.62% and 19.08%, respectively. In heavy soils, the longer the length of the border increases, the method is more accurate in estimating Philip's coefficients, while in light soils, the advancing time, which in addition to length is a function of permeability, input discharge, and the slope of the bottom of the bed is increased, the accuracy of the method in estimating Philip's penetration coefficients is increased.
S. Ashkevari, S. Janatrostami, A. Ashrafzadeh,
Volume 29, Issue 1 (4-2025)
Abstract
In this study, a conceptual model based on dynamic systems was developed to optimize the management of water, land, and agricultural production (tea and rice) in the irrigation zones of the Sefidroud irrigation and drainage network. To understand the behavior of the network and create a simulation model of the system, a dynamic systems modeling approach was employed, and the simulation was conducted using MATLAB/Simulink. Subsequently, the optimization model of the studied system was developed as a multi-objective model using a genetic algorithm. Various management scenarios were implemented through the weighting of the objective functions. The results showed that selecting the best response from multi-objective optimization models depends on the weighted values of the objective functions, and by changing these values, decision-makers can provide various responses to complex optimization problems. The optimization model determines the cultivated area and water allocation in such a way as to minimize water scarcity and maximize crop performance through different weighting combinations. Furthermore, the findings indicate that the canals of the irrigation network play a crucial role in meeting water needs, and equitable water allocation is essential to prevent excessive extraction and negative consequences, such as saline intrusion and land subsidence. The study demonstrates that the best solutions are contingent upon local conditions and decision-makers' policies. To achieve maximum economic benefits and address water needs, it is suggested to use a weighting combination close to (w1=1,w2=2). Ultimately, this model assists managers and decision-makers in minimizing water scarcity in the region by adjusting cropping levels and optimizing the use of available water resources.
Meysam Bagherifar, Maryam Hafezparast,
Volume 29, Issue 4 (12-2025)
Abstract
The river flow prediction is a key aspect of hydrology that plays a significant role in water resources management, flood risk reduction, and agricultural planning. This study simulates the monthly flow of the Razavar River, located in western Iran, using an extreme learning machine (ELM) model enhanced by the Whale (WOA) Optimization Algorithm and Grasshopper Optimization Algorithm (GOA) metaheuristic optimization algorithms. The data used include river flow, precipitation, evaporation, and temperature, which were collected for 10 years with a monthly time step and normalized in the numerical range of zero to one. 80% of the data is used for training, and the remaining 20% for model evaluation. The performance of the models is measured with the statistical indices RMSE, NSE, and R². First, the basic ELM model is developed using the trial-and-error method to adjust the weights between the hidden and output layers. Then, the WOA and GOA algorithms are used to optimize the weights. The results show that the basic ELM model performs worse than the optimized models (Train: RMSE=0.1427, NSE=0.7795, R²=0.7911, Test: RMSE=0.1406, NSE=0.7811, R2=0.7916). While the WOA-ELM and GOA-ELM models provide similar results, the WOA-ELM model shows better performance in complex conditions (Train: RMSE=0.1215, NSE=0.7869, R2=0.7932, Test: RMSE=0.1165, NSE=0.7872, R2=0.7933). The results of this research show that meta-heuristic optimization algorithms play an important role in improving the performance of river flow prediction models due to their ability to search comprehensively and avoid getting stuck in local optima. The findings of this study emphasize the importance of applying these techniques in water resources management and sustainable planning and will pave the way for future research in this area.
Amir Mahdi Bayat, Mohammad Shayannejad, Mahmood Akbari,
Volume 30, Issue 1 (3-2026)
Abstract
Mathematical models are a suitable tool for surface irrigation design. The EDOSIM model, as a surface irrigation simulation-optimization model, utilizes simulation with the volume balance model and meta-heuristic optimization. In this study, with the aim of improving the simulation of the advanced phase in the EDOSIM model, the Full Hydrodynamic model was replaced by the Volume Balance model for furrow irrigation design, leading to the development of the EDOSIM-HD model. The Saint-Venant equations were discretized using the implicit Preissmann’s finite difference scheme and transformed into a set of nonlinear equations in the form of a system of equations. The resulting system of equations was linearized using the Newton-Raphson method and solved using the Sparse matrix method. The results were compared with the SIRMOD software to validate the simulation. Using the particleswarm solver of the MATLAB software optimization toolbox, the inflow rate as a decision variable was used to optimize the hydraulic objective function consisting of efficiency, adequacy, and uniformity. The results in the experimental field showed that in the initial simulation with an inflow rate of 1.4 lps, important irrigation times, infiltration volume, performance indicators, profiles, and hydrographs showed a deep percolation loss of about 50 percent of water. Also, the results of the EDOSIM-HD model were closer to the Hydrodynamic model of the SIRMOD software than the EDOSIM model. By optimizing and increasing the optimal flow rate (1.8 lps) compared to the initial inflow rate, the advance, cut-off, depletion, and recession times were reduced, and the required infiltration time remained unchanged. The reduction in infiltration volume was also achieved by applying higher inflow rates in less time. All performance indicators also moved closer to their optimal state. Except for Tail Water Ration (TWR), which showed a slight increase of 11 percent (due to higher inflow rate), was negligible compared to the sharp 22% reduction in Depth Percolation Ratio (DPR), and 10% increase in Application Efficiency (Ea). Totally, according to the performance indicators obtained in the validation with the SIRMOD, the simulation of the EDOSIM-HD model was better than in the EDOSIM model in the advanced phase of furrow irrigation design
Reza Peykanpour Fard, Ferial Farasat, Sohrab Hasheminejad, Sima Fakheran,
Volume 30, Issue 2 (7-2026)
Abstract
This study aimed to optimize the site selection of artificial groundwater recharge zones in the Yazd-Ardakan watershed (covering an area of 116,765 hectares) to address challenges such as water scarcity, severe groundwater depletion, and annual rainfall below 100 mm. The research integrated Geographic Information Systems (GIS) and Multi-Criteria Decision-Making (MCDM) methods. Seventeen influential criteria, including elevation, slope, land use, geology, soil type, climate, distance from faults, and isothermal lines were analyzed using ArcGIS 10.5. Criteria weighting was performed using the Best-Worst Method (BWM), and layer integration was achieved through the Weighted Linear Combination (WLC) approach. Results indicated that slope (weight: 0.203), elevation (correlation >0.75), and land use (correlation ≈0.5) had the highest impact on zone suitability, while climate and isothermal lines were less influential. The final suitability map (900×900 m resolution) revealed that central and southern plain areas with slopes <2%, permeable alluvial formations, flat topography, and optimal distance from faults were prioritized for artificial recharge. Sensitivity analysis identified eight key criteria (correlation >0.5), and by eliminating 53% of non-essential parameters, an efficient framework for sustainable water resource management was established. This study not only contributes to raising groundwater levels, improving soil fertility, and preserving local ecosystems but also offers a practical solution for water crisis management in arid regions.
Omolbani Mohammadrezapour, Hadi Siasar, Mohammad Javad Zeinali, Mohammad Nazeri Tahroodi,
Volume 30, Issue 2 (7-2026)
Abstract
Accurate monitoring of water surface dynamics in semi-arid regions poses challenges due to uncertainties regarding the optimal spectral index and sensor selection for effective water resource management. This study assessed the comparative performance of nine spectral water indices across the Landsat-8 and Sentinel-2 platforms to identify the best index-sensor combinations for monitoring semi-arid reservoirs. Utilizing the Google Earth Engine cloud computing platform, 181 satellite images were processed for Golestan Dam in northeastern Iran, comprising 107 Landsat-8 scenes from 2013 to 2023 and 74 Sentinel-2 scenes from 2018 to 2024. After applying atmospheric corrections using the LEDAPS and Sen2Cor algorithms, nine spectral indices (NDWI, MNDWI, ANDWI, AWEI, WI2015, WI1, WI2, LSWI, and NDTI) were calculated and evaluated against the WI2 reference through RMSE, R², and Nash-Sutcliffe efficiency metrics. MNDWI showed superior performance for Sentinel-2 (RMSE=21.42 ha, R²=0.998, NS=0.996), while ANDWI was optimal for Landsat-8 (RMSE=54.54 ha, R²=0.977, NS=0.976). Time-series analysis revealed a 35% reduction in mean annual reservoir area, decreasing from 7.28 km² in 2014 to 4.72 km² in 2021. Consistent seasonal patterns were observed, with spring maxima (9.33 km² in March) and autumn minima (3.35 km² in September) evident across both sensors. A high inter-sensor correlation (r = 0.933) supports the potential for multi-sensor integration in comprehensive monitoring efforts. The LSWI and NDTI indices displayed systematic overestimation due to interference from soil moisture and vegetation, making them unsuitable for quantifying water area. These findings highlight the sensor-dependent nature of optimal index selection, recommending MNDWI-Sentinel-2 pairing for short-term monitoring and ANDWI-Landsat-8 for long-term trend analysis in the management of semi-arid reservoirs.
