genetic algorithm

In arid and semi-arid countries such as Iran, the uneven spatial and temporal distribution of rainfall necessitates a reliance on irrigated agriculture for food production. Consequently, a substantial portion of water resources is allocated to agriculture. Identifying strategies reducing water consumption and improving its efficiency in agriculture are critical priorities. This study employs crop rotation as a key variable in an optimization framework to calculate a matrix of impact coefficients based on insights from expert farmers. The matrix quantifies the effects of sequential crop planting. These coefficients are incorporated into a water allocation optimization model aimed at maximizing economic profitability, utilizing a genetic algorithm and the AquaCrop plug-in program. For this purpose, C# coding within Visual Studio was used to optimize three-, four-, five-, six-, and seven-year rotations involving wheat, soybean, tomato, potato, corn, alfalfa, barley, and sugar beet. Moreover, the impact of crop rotation on crop yield, water allocation, and expected profitability per unit area was evaluated using a valuation formula. Rotation Optimization results indicated that the four-year rotation (sugar beet, corn, potato, tomato) achieved the highest economic profit, while the seven-year rotation was most effective in reducing water allocation (by 9.45%). Therefore, crop rotation optimization is a significant parameter for enhancing crop yield, boosting profitability, and achieving long-term water savings.

In this study, the country's water resources management system has been modeled using a system dynamics approach. After simulation, optimal water resources, social power of governance, government financial resources, and the rate of water migration have been determined based on the decision variables of household, agricultural, and industrial water tariffs. The optimal combined results suggest an 11%, 200%, and 200% increase in water tariffs for household, agricultural, and industrial sectors, respectively, over the next 5 years.

Initially, this study modeled and simulated the variables and feedback loops involved in water resources management using system dynamics tools. For optimization, two methods, genetic algorithm and the Simulation-optimization capability in the Vensim software, were employed. In the genetic algorithm, an initial population of 30 individuals and crossover and mutation probabilities of 83% and 17% were considered. The selection criterion for each chromosome is based on the roulette wheel method.

The results indicate that optimization using the genetic algorithm results in an 89% increase in water resources behind dams compared to the baseline conditions, whereas the Vensim software output shows an 84% improvement. Overall, the genetic algorithm improves the objective function by 2% compared to the Vensim software's optimization output. Additionally, after optimization, the best tariff policy for enhancing the objective function is a further increase in agricultural and industrial water tariffs compared to household tariffs.

Optimum operation of dam reservoirs is one of the most significant management factors in developing the annual resource and consumption plan of dam reservoirs during operation. The decisions regarding amount of water release are made by having the volume of  the reservoir, amount of demand, and the prediction of reservoir inflow in the actual operation of dam reservoirs. Since the volume of release is related to the storage volume of the reservoirs of the dams and should be optimized simultaneously, after introducing the genetic algorithm and the particle swarm algorithm, the performance of these algorithms alone and in combination with each other in the optimal operation of the Alavian dam reservoir are compared with the modeling results in the nonlinear programming and the rule curves of the operation are developed in this study. The performance indicators of the reservoir were been used including reliability, vulnerability and stability  to evaluate the performance of the examined algorithms in the optimal operation of the reservoir.

In this study, after introducing the genetic algorithm and the particle swarm algorithm, innovatively examines the accuracy and effectiveness of modeling by comparing the performance of these algorithms both individually and in combination. This comparison focuses on optimizing the operation of the Alavian dam reservoir over multi-step ahead, using modeling results from the software Lingo. To enhance decision-making for improved management of the Alavian dam reservoir, operation rule curves have been developed. The model utilizes a series of 25 years of data from the Alavian dam, which includes the volume of inflow, the volume of release from the reservoir, storage volume, and usage data encompassing drinking, agriculture, industry, and environmental needs. Additionally, information such as the volume of overflow from the dam reservoir and the volume of evaporation from the surface of the Alaviyan Dam reservoir has been collected on a monthly basis.

The results from these optimal solutions indicate that the combined algorithm outperforms other methods, demonstrating a better correlation with the reservoir management policy. Over the last 25 years, the combined algorithm met 85% of the water requirements for agriculture downstream of Alavian dam, compared to 82% for the Particle swarm optimization(PSO)  algorithm and 78% for the genetic algorithm (GA). In contrast, the nonlinear programming (NLP) method met 80%. The total shortages over the entire 25-year operational period for the GA, PSO, GA-PSO, and NLP algorithms were 38, 33.7, 27.1, and 35.2 million cubic meters, respectively. The GA-PSO algorithm has successfully addressed 10.87 million cubic meters more than the GA algorithm and 6.57 million cubic meters more than the PSO algorithm.

Investigating the results obtained from the optimal solutions revealed that the hybrid algorithm model provides a more favorable result and shows a better correlation regarding the reservoir operation policy. The results indicate the high performance of the hybrid algorithm compared to other studied methods in the optimal operation of the single reservoir system of Alavian dam. Accordingly, the optimal parameters of the Alavian dam reservoir were obtained using a hybrid algorithm. It was proposed to  release volume rule curves and reservoir volume for the  multi-step ahead.

In order to effectively manage soil and water resources, it is imperative to investigate wet aggregate stability (WAS) as a fundamental indicator for assessing soil structure and quality. In this study, machine learning techniques, specifically random forest (RF) and random forest optimized with genetic algorithm (GA-RF), were employed. The analysis focused on determining the texture, organic matter content, and lime characteristics of 55 soil samples collected from the Arsbaran forests. Utilizing various input combinations based on correlations with WAS, modeling was performed across seven distinct scenarios. Furthermore, three performance metrics including correlation coefficient (CC), normalized root mean square error (NRMSE), and Wilmot coefficient (WI) were utilized to evaluate the effectiveness of the models. The findings indicated that the RF5 model exhibited superior performance among the random forest models, achieving NRMSE = 0.038, CC = 0.736, and WI = 0.789. Similarly, the GA-RF5 model, optimized through a genetic algorithm approach, demonstrated exceptional performance with NRMSE = 0.031, CC = 0.800, and WI = 0.842 when considering input percentages of sand, silt, and clay. Moreover, results from RF1 (NRMSE = 0.047, CC = 0.589, WI = 0.721) and GA-RF1 (NRMSE = 0.036, CC = 0.662, WI = 0.797) emphasized that clay content exhibited the strongest correlation with stability. Additionally, the incorporation of calcium carbonate equivalent in scenario 7 significantly enhanced model performance and positively influenced the prediction of wet aggregate stability. In summary, the hybrid model combining random forest with a genetic algorithm is recommended for precise and reliable determination of wet aggregate stability in studies focusing on soil properties.

AbstractHydropower plants are one of the renewable energy sources with higher efficiency than other renewable sources. One of the appropriate methods to maximize efficiency in these power plants is metaheuristic methods. The purpose of this study is to calculate the maximum power generation from the Karoun Basin reservoirs, so that there is no disruption in the supply of downstream demands. Thus, the bacterial foraging optimization algorithm (BFO) and genetic algorithm (GA) have been used to optimize power generation. The results showed that it is possible to have maximum hydropower energy production with 75% reliability in downstream water supply. According to the results of GA and BFO, there is an annual hydropower energy production capacity of 18347 and 18431 GWh, respectively, in the study area, the majority of which is related to the Godarlandar dam on the Karoun river. The Dez river share in power generation, according to the GA results, is 2632.4 GWh and, according to the BFO results, is 2645.2 GWh. Comparing the performance of GA and BFO, as the objective function becomes more complex, BFO shows a higher ability in more accurate calculations.

Prediction of effluent quality parameters of wastewater treatment plant is essential in managing water resources, limitation of fresh water resources in the world, furthermore the ever-increasing population growth and the development of urbanization, have made the approach of urban wastewater reuse inevitable. In such a situation, the use of recycled water can be considered as one of the ways to overcome water shortage and prevent wastage of water resources. This research aimed to investigate the performance of Prediction of effluent quality parameters of wastewater treatment plant in using recycled water. On the other hand, due to the health hazards caused by the discharge of wastewater from wastewater treatment plants to water sources, achieving a precise design and correct management of wastewater treatment plants (WWTP: Wastewater Treatment Plant) is one of the important challenges of sustainable water resources management. 4 effective parameters including (BOD), (COD), (TSS) and (pH) of wastewater, were selected as input to the model, during a statistical period of 3 years (1397 to 1399). Also, 70% and 30% sizes were determined as the best sizes for training and testing stages in order to model BODeff and CODeff parameters. In this study, multi-Layer perceptron models (MLP), basic radial neural network models (RABF), as well as the integration of these models with several other algorithms, such as genetic algorithm (GA), particle swarm optimization algorithm (PSO) and sine cosine algorithm (SCA), were used in order to predict the quality parameters of wastewater treatment plant effluent.

Ensuring the health and sustainability of the environment is one of the secondary goals of hydraulic structures. Water pollution and the reduction of its quality are serious threats to the health of the earth and will turn rivers into channels for polluted industrial wastewater. Increasing the residence time of flow in rivers by constructing hydraulic structures such as gabion spur dikes can increase the self-purification capacity. In this research, a laboratory investigation of pollution transport using NaCl tracer material in a laboratory channel with a sediment bed with a medium diameter (D50) of 11.85 mm, thickness of 12 cm, and length of 12 meters in a different number of fine-grained or coarse-grained gabion spur dikes from 1 to 4 were performed. Analytical solutions of Advection-Dispersion equation (ADE), OTIS model, and solution of Transient Storage Model (TSM) were used for numerical simulation. Laboratory results showed that gabion spur dikes with fine-grained materials reduce the peak concentration of pollution (Cmax) compared to coarse-grained materials. The results showed that the gabion spur dikes in the flow path increase the residence time of the pollutant by increasing hyporheic exchanges. The advection-dispersion equation (ADE) has low accuracy in the simulations due to the lack of attention to the role of the storage zone exchanges, with a determination coefficient (R2) of 0.71 to 0.83. On the other hand, the OTIS model, which is based on the transient storage model (TSM), considering the role of the porous zones of spur dikes in the transient storage of solute, has high accuracy with a determination coefficient (R2) of 0.91 to 0.98. The estimated coefficients of longitudinal dispersion (Dx) and storage zone exchange (α) by the temporal moment analysis were evaluated lower than the estimates of OTIS model in most of the simulations.

Urban flooding is caused by the lack of capacity of the harvesting channel network and occurs when the amount of precipitation exceeds the network's capacity. One of the two main factors contributing to the aggravation of damage caused by urban floods is population growth and the expansion of urbanization, and the second factor is heavy rainfall caused by climate change, which plays an essential role in intensifying and accelerating the hydrological cycle and may change the amount and frequency of precipitation. This factor affects the probability of flooding, runoff volume, and peak flow. It is more visible in arid and semi-arid areas where rainfall usually occurs briefly but with high intensity. Urban flooding is a growing threat to urban infrastructure and public health, posing significant challenges to urban resilience and sustainability. One of the urban infrastructures that is of great importance is the runoff collection network. The increase of impervious surfaces wear and tear on the network. changes in the rainfall pattern due to climate change have increased the occurrence of urban floods and raised the importance of network redesign to minimize the system vulnerability.

In this research, the runoff harvesting network of ten districts of Tehran Municipality was redesigned and optimized. This area, with a population of 327,000 people, is located in the relatively dense fabric of the Tehran metropolis, and its area is 807 ha. Simulating the runoff collection network and checking the performance of the network by two indicators of vulnerability and reliability requires an accurate model with great details. For this purpose, in this research, SWMM version 5.1 software was used to simulate the runoff collection network. The study area was divided into 285 sub-basins to simulate the sub-channels in better detail. Then, information such as slope, area, and percentage of impervious space was introduced through ArcMap software version 10.3.1 as information under the watersheds. The sub-watershed width parameter was calculated by dividing the sub-watershed area by its most significant length using Q-GIS software and applied to the sub-basins. The LARS-WG model has also been used for the exponential micro-scale output of climate models. To simulate the network in the current or present situation, the historical precipitation information of the Mehrabad synoptic station was used, and to affect the network in future conditions, the precipitation information of the climate models of the sixth climate change report was used. Among the predictions of climate models, the most incremental changes in threshold precipitation were selected as a pessimistic scenario, and a system redesign was done to reduce vulnerability under this scenario.

This study was conducted to assess the performance of Tehran municipality's runoff collection network under current and future conditions. The SWMM hydraulic model was employed to simulate the network under various rainfall scenarios. Current conditions revealed a total runoff volume of 45.9, 51.14, and 59.7 thousand m3 for return periods of 2, 5, and 10 years, respectively. This runoff volume resulted in a vulnerability increase from 10.4 to 12.2% and a reliability reduction from 97.5 to 95.8%. To evaluate the network's performance under future climate change scenarios, the SWMM model was used with data from the IPCC sixth assessment report. Among the top five climate models, the one with the highest precipitation was selected as the pessimistic scenario. Simulation results under future conditions indicated a significant runoff volume increase, reaching 64.04 and 72.18 thousand m3 in 5- and 10-year return periods, respectively. This increase corresponded to vulnerability indices of 12.7 and 13.9% and reliability indices of 95.3 and 94.3% for the same return periods. To enhance the network's resilience, a genetic algorithm-based optimization approach was employed. Cost, reliability, and vulnerability index were considered optimization objectives with specific weighting functions. The algorithm converged to an optimal design solution in the 168th iteration, resulting in a 7.6% vulnerability reduction and a 98.1% reliability enhancement.

The vulnerability index in the return periods of 5 and 10 years is equal to 12.7 % and 13.9 %, respectively, and the reliability index is equal to 95.3 % and 94.3 %. After checking the network in its current state and future conditions, an optimal network redesign was done to improve system performance in both current and future conditions. For this purpose, the genetic algorithm was used for optimization, and the objective function consisting of cost, vulnerability index, and reliability index and the importance weights of each, were defined. Then, MATLAB software did the optimal network redesign by connecting the simulator and optimizer model. The results showed that in the 168th iteration, the algorithm reached its final answer of 0.3, which remained constant until the 300th iteration. Also, the optimal redesign has reduced network vulnerability by 7.6% and increased reliability by 98.1%. This research showed that with an optimal redesign and solving the current network problems, the system's ability to face future climate change threats could also be increased. Of course, spending the least money to achieve the best result was one of the main goals of this research. In future studies, it is recommended to use low-impact development tools along with optimal redesign to fix defects and improve the performance of the runoff collection network.

Weirs are a type of water structures that are used to pass floods, control water levels and measure flow rates. The accurate and appropriate design of the weir is of particular importance, and the existence of weirs with inappropriate discharge capacity is one of the most important factors of channel inefficiency. Circular crest weirs are reliable weirs for use in channels that are built perpendicular to the flow or at an angle (oblique) to the direction of the flow. Oblique weirs are used to increase the effective length of the weir and increase the water-passing capacity and hydraulic parameters such as the discharge coefficient. On the other hand, the study of the flow in the unsteady state, in which the flow variables in the channel vary with time, is of special importance in hydraulic science. In this research, the calibration of the discharge coefficient of the oblique circular crest weir under unsteady flow condition has been discussed. Also, the effect of changing the height of the weir and changing the flow pattern on the discharge coefficient of the unsteady flow in this type of weir has been investigated.

In this research, three oblique circular crest weirs with a height of 15, 10 and 20 cm and other identical geometric parameters under free and unsteady flow conditions in the flow range of 25 to 51 liters per second under the range of flow rate changes of 1, 3 and 5 liters per second and the range of time changes of 5, 10 and 15 seconds have been tested. Therefore, the total number of tests was equal to 27. Experiments were carried out in the research flume of Islamic Azad University, Isfahan (Khorasgan) branch with a rectangular cross-section of 10 meters in length and 0.6 meters in width, and weirs were installed at 2.5 meters from the entrance of the channel The flume has no slope of the floor and the height of the flume is the same without change and its walls are made of tempered glass and completely sealed. The mentioned flume is equipped with a closed water circulation system and can provide a maximum flow rate of up to 55 liters per second. The tank of this flume is made of fiberglass and has a cylindrical shape with a volume of 10 cubic meters and is placed underground at a height of about 2 meters. Flow can be regulated by PLC device. The depth gauge of the device without contacting the free surface of the flow, by sending and receiving ultrasonic waves, measures the distance with very high speed and accuracy, and in this way the depth of the flow is recorded in the PLC device. In fact, the most important part of this test set is the PLC device connected to the flume, which controls all flow settings, including the flow rate, maximum flow rate, relevant hydrographs, and records the flow depth data. In this study, the governing relations were first extracted and then the genetic algorithm was used for optimization. The optimization process in the genetic algorithm is based on a guided random process. This method is based on the theory of gradual evolution and Darwin's fundamental ideas. Genetic Algorithm is a non-algebraic optimization method that is suitable for functions whose optimization with algebraic methods is exhausting. This algorithm and other evolutionary algorithms examine the response space in parallel and cluster by cluster and not member by member and for this reason, the possibility of occurrence of local maxima or minima is eliminated. Also, these methods do not need information about the derivatives of the target function and only the main form of the function is required. In this research, the solver available in Excel software was used for optimization with evolutionary algorithm method. After specifying the target variable, which is the discharge coefficient in this research, the evolutionary algorithm option is selected, and the software performs its analysis to calculate the best discharge coefficient with the lowest value for total errors.

The results obtained in this research show that in all patterns of flow changes, increasing the ratio of water height relative to the weir crest to the weir height (H/P) causes a decrease in the discharge coefficient of unsteady flow. Also, in all patterns of flow rate change, increasing the height of the weir causes a decrease in the discharge coefficient of the unsteady flow, that is, the discharge coefficient of the unsteady flow in the oblique circular crest weir is a function of the upstream flow conditions and the height of the weir. Also, the unsteady flow rate coefficient in this type of weir is a function of the flow rate change pattern. As can be seen from the graphs obtained in this research, the flow rate change pattern does not have much effect on the slope of the flow coefficient changes compared to the H/P changes, but it is effective on the degree of influence of the weir height changes for a fixed H/P value. On the other hand, the graphs show that the discharge coefficient of unsteady flow for oblique circular crest weir varies between 0.5 and 1.5, which previous researchers had also concluded that the discharge coefficient of unsteady flow for other weirs is not constant and is a function of the geometry of the weir and the pattern of changes in flow rate.

The purpose of this study is the effects of the formation of water markets on the improvement of water productivity in the Tajen catchment basin. In this research, a mathematical programming model modeling system and profit maximization objective function were used in MATLAB environment. After simulating the formation of the water market, its impact on physical and economic water productivity indicators was evaluated in two groups including: farms without water restrictions group (A) and farms with water restrictions group (B). Based on the results, forming a water market leads to a 13 percent increase in profit in group (A) and 30 percent in group (B). According to the results, the formation of the water market reduces and increases the amount of water consumption in representative farms (A) and (B), respectively. The results of the evaluation of water productivity indicators indicate that in group (A), the availability of water and consequently the increase of the cultivated area, and in group (B), the compensation of the lack of water and the increase of the cultivated area have led to an increase in physical productivity. In the farms of group (A), the sale of water and in the farms of group (B), the allocation of water to products with higher economic value has led to an increase in the economic productivity of water. In general, it can be said that forming a water market leads to an increase in productivity, but alone it does not lead to achieving sustainable agriculture and reducing water consumption at the watershed level. Therefore, the implementation of other policies such as the control of extraction from surface and underground water resources, volume delivery of water based on the pattern of optimal cultivation and allocation of water to products with higher economic value along with the water market approach can improve water productivity in addition to reducing water consumption.

The aim of this research is to optimize the design parameters of underground drainage systems with an economic approach. The study of the relationship between the depth and distance of drain installation has been the subject of many researchers, each of whom has tried to reduce the costs of drain implementation. In this research, genetic and multi-world algorithm optimization model was used to optimize the main design parameters of the underground drainage system of regional agricultural lands around Gorgan city. The area of the construction area of the drainage system was around 200 hectares. In this research, by using genetic and multi-world algorithm, the design parameters were selected in such a way that they lead to the lowest implementation cost of the underground drainage system. In this regard, the design parameters have been selected by combining Hohhot's permanent equation and optimization algorithms.

The area under study is Aliabad district, one of the functions of Gorgan city. The lands of Ali Abad region are also located in the upper part of the plains. These lands are located in eastern longitude and northern latitude. The study area covers about 200 hectares.
Genetic Algorithm:Genetic algorithm is an optimization method inspired by living nature, which can be referred to as a numerical method, direct and random search in classifications. This algorithm is based on repetition and its basic principles are adapted from genetic science.
Multi verse Algorithm:The big bang theory considers the occurrence of a big explosion as the beginning of the origin of our universe. According to this theory, the big bang is the origin of everything in this universe and nothing existed before it. A population-based algorithm performs the search process in two stages: exploration and exploitation. In this algorithm, the concepts of white hole and black hole are used to explore the search space. In contrast, wormholes help exploit search spaces.

Genetic Algorithm:By applying the genetic algorithm optimization model to the data of the studied lands, the optimal parameters were calculated. Table (4) shows the design parameters obtained from the genetic algorithm optimization model in different drainage coefficients for the allowed depth of drain installation. Considering the entire search area (permissible depth of drainage installation from 1.5 to 3.5 meters from the ground surface), the results obtained from the land input data of the studied area showed that taking into account the drainage coefficient of 2.5 mm per day, the minimum cost the equivalent of 51.3 million Tomans has happened in 8 hectares at a depth of 2.62 meters from the ground surface with an installation distance of 79.9 meters and a diameter of 125 mm.
Multiverse algorithm
The optimal parameters were calculated by applying the multi-world algorithm optimization model. Table (7) shows the design parameters obtained from the optimization model in different drainage coefficients for the allowed depth of drain installation. Considering the entire search area (permissible depth of drain installation from 1.5 to 3.5 meters from the ground surface), the results obtained from the input data of Aliabad lands showed that taking into account the drainage coefficient (discharge intensity) 2.5 mm per the minimum cost equivalent to 51.5 million Tomans has happened in an 8-hectare unit at a depth of 2.61 meters from the ground with an installation distance of 79.5 meters and a diameter of 125 mm.
Comparing the results of genetic and multiverse algorithms
Comparing the performance of genetic and multi-world algorithms shows the high efficiency of both algorithms in solving the problem of optimizing the diameter of drainage pipes and their installation depth. Both algorithms have almost the same performance in solving this optimization problem; So that both algorithms in the distance of the impervious layer to the depth of the drain installation (D) is equal to 2.25 meters, the stabilization depth of the water table (in the middle of the two drains) from the ground level (H) is equal to 1.5 meters and intensity Drainage or drainage from the surface unit (q) is equal to 4 mm per day, and they have calculated the optimal depth of drainage installation to be 13.3 meters

The design of drainage systems consists of choosing three parameters: depth, diameter and distance of drains. Various approaches can be used to choose the best combination of three parameters. One of the approaches can be to reduce the costs of implementing the drainage system. In this research, using the genetic algorithm, these parameters were selected in such a way that they lead to the lowest implementation cost of the drainage system. In this regard, the design parameters have been selected by combining Hohhot's permanent equation and genetic algorithm.

Water resources management, especially irrigation practices, is heavily reliant on reference evapotranspiration (ET0). ET0 is the rate of evaporation and transpiration from a standard reference surface with a presumed surface resistance of 70 s.m-1, the height of 0.12 m and an albedo of 0.23. Penman-Monteith FAO-56 (P-M FAO-56) approach is the most commonly used method for calculating ET0. In spite of the fact that FAO-PM is achievable, its implementation remains inconvenient because it requires a large amount of meteorological data, which is derived from standard meteorological observation stations. In the absence of complete climate data, it is highly desirable to have a model with fewer input climatic dates. Therefore, remote sensing methods have been used and improved over time to estimate ET0 at various spatial scales. Alternatively, it has been observed that the research community has become increasingly interested in obtaining data from metaheuristic algorithms that are based on artificial intelligence (AI).

In this research, it has been attempted to estimate the amount of daily reference evapotranspiration (ET0) using two data-driven models, using a combination of inputs from meteorological station data and satellite imagery data from MODIS sensor, by considering different inputs from these sources. The models include the random forest (RF) and hybridized RF with genetic algorithm optimization (GA-RF). Moreover, the correlation of input variables with ET0 is evaluated and the possibility of training a simple and accurate machine learning model in the conditions of lack or absence of meteorological data using satellite image data is investigated. So, this study aimed to determine ET0 in the time period of 2003-2021 using land surface temperature (LST) data and leaf area index (LAI) acquired from MODIS sensor and Tabriz meteorological station data including maximum and minimum air temperatures (Tmax, Tmin), average temperatures (T), wind speeds (U2), average relative humidity (RH), maximum and minimum relative humidity (RHmax, RHmin), and sunny hours (n). For the study area, daily LST were extracted from the Terra (MOD11A1) and Aqua (MYD11A1) satellites. Moreover, the LST of Terra and Aqua satellites were combined, since the LST values had missing data due to the presence of clouds. Furthermore, MODIS MCD15A3H version 6.1 using four-day data from Terra and Aqua satellites was used to determine the leaf area index (LAI). The standard P-M FAO-56 method for calculating daily reference evapotranspiration was considered as the base method. The set of input parameters was considered based on the cross-correlation of the parameters with reference evapotranspiration obtained from the FAO-Penman-Monteith equation.

The results of two data-driven models including standalone random forest (RF) and hybridized RF model with genetic algorithm (GA) to estimate ET0 values were compared with calculated ET0 by P-M FAO-56 equation. The results indicated that all of the studied input variables are highly correlated with the target variable. Based on the P-M FAO-56 method, the average air temperature with the highest value (R2=0.903) and the wind speed with the lowest value (R2=0.282) has a high and low correlation with reference evapotranspiration. Also, by comparing LAI and LST MODIS parameters, LST has the highest correlation coefficient with ET0 with R2=0.865. A total of twelve scenarios for estimating ET0 are evaluated, each with a different set of input parameters. Based on the correlation between the parameters and ET0, the first ten scenarios are categorized. Additionally, the eleventh scenario is based only on satellite images, and the twelfth scenario is based solely on weather station data. Based on the results, the GA-RF-10 (R2=0.976, RMSE=0.200, MAPE=11.373, and MBE=0.028), which includes all input parameters, outperforms the other models. There was a greater degree of accuracy with the RF-10 (R2=0.949, RMSE=0.293, MAPE=16.442, and MBE=0.017) when compared with the other random forest models. Based on the comparison of scenario 11 (satellite image data) and scenario 12 (meteorological station data), it appears that scenario 12 is more accurate for both RF (R2=0.922, RMSE=0.357, MAPE=20.712, and MBE=0.009) and GA-RF (R2=0.944, RMSE=0.306, MAPE=17.037, and MBE=0.013) models. Despite the fact that only satellite image parameters did not provide accurate estimation of ET0 compared to independent meteorological parameters, the inclusion of these parameters in the ET0 estimation resulted in more acceptable results, demonstrating the importance of satellite image parameters. Thus, satellite data may be useful and recommended for estimating ET0, particularly in areas without meteorological stations.

Reference evapotranspiration (ET0) is an important parameter in the interactions among soil, vegetation, atmosphere, surface energy and water. Direct measurement of evapotranspiration values is costly and time consuming. On the other hand, modeling this complex process in which many variables interact with each other is not feasible without considering multiple assumptions. In this regard, the FAO Penman-Monteith method is used in a wide range of climatic and environmental conditions. One of the weaknesses of FAO Penman-Monteith method is its dependence on various meteorological parameters. Therefore, it is necessary to implement methods with lower meteorological variables that can estimate ET0 with suitable accuracy. Thus, in the present study, an attempt was made to estimate ET0 with acceptable accuracy using machine learning models.

In the present study, daily meteorological parameters in the time period of 2000-2020 including maximum and minimum air temperature (Tmax, Tmin), mean temperature (T), wind speed (U2), average relative humidity (RH), maximum and minimum relative humidity (RHmax, RHmin) and sunshine hours (n) were obtained on a daily basis in three stations of East Azerbaijan province (Tabriz, Sarab, and Maragheh). Moreover, six scenarios were defined as input combinations. Then, using random forest (RF) method in two cases: Single random forest and using the genetic algorithm (GA) to optimize its effective parameters with considering the FAO Penman-Monteith model as a basis, the machine learning models were calibrated and validated for estimating ET0 values at studied stations. Furthermore, the performance of empirical equations in three groups based on temperature (Hargreaves, Blaney-Criddle and Romanenko), radiation (Irmak) and mass transfer (Meyer) were also investigated. It should be noted that 75% of the data were considered for calibration and 25% for the validation of machine learning methods. Finally, using the statistical criteria of correlation coefficient (CC), scattered index (SI) and Willmott’s Index of agreement (WI), a suitable machine learning method was introduced to estimate the reference evapotranspiration. Also, the most suitable combination of meteorological parameters for ET0 estimation was suggested.

The obtained results showed that in all studied stations, scenario 6 has the best performance, either in the case of single random forest (RF) or in the case of random forest optimized by genetic algorithm (GA-RF). Meteorological parameters of this scenario include minimum and maximum air temperature, minimum and maximum relative humidity, sunshine hours and wind speed. By optimizing the RF-6 parameters with the genetic algorithm at Tabriz station, the statistical criteria were improved (CC from 0.990 to 0.991, SI from 0.103 to 0.098). At Sarab station, the CC was increased from 0.980 to 0.982, the SI was decreased from 0.140 to 0.132 and the WI was increased from 0.989 to 0.990. At Maragheh station, CC was increased from 0.990 to 0.991, SI was decreased from 0.103 to 0.098 and WI remained unchanged at 0.995. In general, the decreasing trend of the scattered index for RF method from scenarios 1 to 6 can be understood by increasing the input parameters of the random forest method. Among the three groups of empirical methods based on air temperature, radiation and mass transfer for estimating ET0, the best performance was seen for the Blaney-Criddle method based on air temperature. In all studied stations, the GA-RF model showed better performance than the empirical methods. Also, GA-RF-5 with similar meteorological parameters with Blaney-Criddle method provided accurate ET0 estimations.

Determining the amount of daily evapotranspiration and consequently accurate estimation of water requirement of plants provide the basis for proper designing of irrigation systems by reducing installation costs and providing a suitable program for the use of water resources in the agriculture sector. So, in the present study, meteorological data from Tabriz, Sarab and Maragheh stations were used to evaluate the ability of machine learning methods including RF and GA-RF to estimate the values of reference evapotranspiration. The results showed the high accuracies of RF-6 and GA-RF-6 for all studied stations and Belany-criddel among the empirical models. In a more detailed look, the genetic algorithm had positive effects on increasing the model accuracies by reducing scattered index of GA-RF scenarios 1, 4, 5 and 6 in Tabriz and Maragheh stations as well as scenarios 1, 5 and 6 at Sarab station. Finally, it can be concluded that both RF and GA-RF models provided the most accurate estimates of daily reference evapotranspiration in the East Azerbaijan province.

Hydropower energy management is essential under climate change conditions. In this research, Slime Mould Optimization Algorithm (SMOA) is used to optimize the system of three hydropower reservoirs, and its results are compared with Genetic Algorithm (GA) to evaluate the performance of SMOA. First of all, SMOA performance is measured for Akley test function, which has been successfully performed. In the next step, the optimization is used on the three-reservoir hydropower system of Sazbon reservoir, Seymareh and Karkheh stream located in the Karkheh basin (Iran). The optimization of the hydropower problem is carried out for the baseline period of 1976-2005 and the future period of 2040-2069 under the RCP8.5 climate change scenario. The objective function is to minimize the lack of hydropower supply. For the optimization of the three-reservoir hydropower problem, the results show that the value of the objective function based on SMOA is close to the absolute optimal value, especially in the period of climate change. In general, the performance of SMOA to achieve the optimal value of the objective function in climate change periods is better than the baseline period and the solutions are more stable. In the comparison between SMOA and GA for the three-reservoir operation mode in the baseline and future period under the RCP8.5 scenario, the performance of SMOA in reaching the desired value of the objective function is much better, the speed of convergence is higher, the run-time is shorter, and the solutions of the objective function are more stable.

Nowadays, numerical modeling is known as a powerful method and tool in investigating practical phenomena. So that this method is used in many fields of engineering. Although the numerical model of an engineering problem is usually prepared based on several simplifying assumptions, it is possible that this numerical model does not reflect all aspects of the real problem and is not able to show the real behavior in reality. Direct updating methods use the analytical solution of the problem for this purpose, although these methods specify the necessary corrections without the need to repeat calculations and in one step, but in most cases such corrections do not have a physical meaning in reality but iteration-based updating methods require sensitivity analysis of the effective parameters in the problem in order to find the impact of each of them. One of the solutions to deal with the time-consuming problem of multiple re-analyses in numerical models prepared by commercial software during model updating based on sensitivity analysis is to replace the numerical model with an approximate representative model known as meta-models. The response surface method is one of the common methods for building such meta-models. The response surface technique is actually a test design method to select the design parameters in the experiment with the aim of optimizing some system response functions. From this point of view, the research regarding the response of the network of open channels to the arrival of the flood wave and predicting the characteristics of the flow in the branches of the network can be the solution to minimize the damages caused by it.

In this research, in order to update the numerical model of unsteady flow in the network of open channels by using the combined method of response surface and meta-exploration methods, a branch of the Garmabadr River (Ziquon) has been selected, and the desired data has been prepared and sorted. have been Hec-Ras software has been used as a numerical method. Neural network is also chosen for simulating the numerical method so that the desired parameters can be optimized in the process of using the genetic algorithm method. This area is located in the northern part of Iran and in the northeast of Tehran province. The geographical coordinates include longitude 51 degrees 32 minutes to 51 degrees 38 minutes and latitude 35 degrees 51 minutes to 35 degrees 58 minutes. MATLAB software has been used to build a meta-model or an alternative model. MATLAB is a software that can be called the language of mathematics and modern engineering sciences.

As it is clear from the results of the Hec-Ras program, the discharge of stations number 13 and 22 is equal to 308.03 and 27.89 cubic meters per second, respectively. The estimation error is equal to the difference between the estimated flow rate and the observed flow rate, which is 18.03 and 2.11 cubic meters per second, respectively. The percentage of the estimation error is equal to the ratio of the estimation error to the observed value is equal to 6.2% and 7%, respectively, which is an acceptable value for this research, and this result shows the appropriate performance of the genetic algorithm for optimizing the flow simulated by It shows the neural network, on the other hand, the low percentage of error also determines the accuracy of the intended program. As a result, the error of the genetic program is at an optimal level. As it is known, Manning's coefficients are very important and influential parameters in the behavior of the flow, the change in which causes significant changes in the simulation results. The effect of this uncertainty on the performance of numerical methods was clearly identified in this research. As a result, examining inputs that have inherent uncertainty and determining their appropriate values is a suitable solution for improving numerical methods.

 Genetic algorithm is a powerful method to determine the values of effective parameters in simulated flow performance, and its combination with neural network is a powerful tool for engineering purposes, including the analysis of the behavior of open channels, whose fast convergence is a strong point to choose in these problems. in similar cases.
 Manning's coefficients are very important and influential parameters in the simulated flow behavior, with a small change in it, the simulated model undergoes significant dispersion, which should be considered in the analysis process so that the results are not significantly different from reality.
 Optimizing an effective parameter in simulated flow behavior is an erosive and time-consuming process, and the use of neural network becomes much faster and more reliable due to the high convergence speed of this process.

The analysis of transient flow plays a critical role in designing pipe systems and pipe networks. Controlling and collecting the pressure wave signals at proper spots in the pipeline can provide much information about the system. Many researchers have studied transient flow and the loss caused by unsteady flow (Brunone et al, 1991; Pezzinga, 1999; Vitkovsky et al., 2000).
Compared with steel pipes, the use of polymer pipes such as polyethylene (PE) and PVC in pipelines and pipe networks has attracted much attention due to their superior properties. Researches have been conducted on the dynamic behaviour of these pipes on transient flow. Brunone et al. (1995) explained that pressure wave damping in a polyethylene pipeline is caused by unsteady friction loss; however, the research showed that there is a large difference between the numerical and the experimental results, and this is due to the viscoelastic effects of polymer pipe walls, which were neglected in this study. Soares et al. (2008) examined the viscoelastic behaviour of PVC pipes on transient flow. The creep function of these pipes was calculated by inverse solution of the transient flow. The results showed that the damping, scattering, and shape of the transient pressure waves are fully described by taking into account the viscoelastic behaviour in the developed numerical model. Carriço et al. (2016) studied the uncertainties of the transient flow numerical model in polyethylene pipes, indicating that unsteady friction loss and viscoelasticity of polyethylene pipe walls have parallel effects on transient signals and the effects cannot be simultaneously distinguished.
Most studies have been so far conducted on the transient flow in a simple pipeline made up of steel and concrete. Since few research has been done on transient flow in more complex systems and plastic pipes, the present paper investigates the numerical and experimental model of transient flow and its properties in polyethylene pipe networks in time domain. In this study, by collecting transient signals of the pipe network, unknown parameters are calibrated and extracted by inverse analysis of the transient flow for different discharges. The pressure signal properties in polymer pipes are also compared with discharge variations.

Wind erosion is a key process in land degradation worldwide, especially in arid and semi-arid regions of Iran. This phenomenon is affected by many soil characteristics. The main objective of this study was to estimate the wind erosion threshold velocity using easily measurable soil characteristics along with data mining methods. For this purpose, wind erosion threshold velocity was measured in 100 areas in Fars province using a portable wind tunnel. Wind erosion threshold velocity was predicted by a support vector regression algorithm using easily measurable soil properties. In this regard, a genetic algorithm was used in order to obtain a set of parameters effective in estimating wind erosion threshold velocity. The results showed that the characteristics of soil moisture (r = 0.77), the size distribution of soil particles including the mean weight diameter of aggregate (r = 0.87) and the wind-erodible fraction of soils (r = -0.81), penetration resistance (r = 0.75), and organic matter (r = 0.33) have a high and significant correlation with wind erosion threshold velocity and play a key role in determining the threshold velocity of wind erosion in the region. According to the evaluation criteria, the combined support vector regression model with the genetic algorithm had the best performance and the most accurate estimate for wind erosion threshold velocity (RMSE = 0.53 and R2 = 0.92) and can be a promising method for estimation of wind erosion threshold velocity.

Water distribution network analysis is traditionally done under the assumption that the demand requirements are met at all the nodes. In reality, a network could be met with various abnormal and uncertain conditions, resulting in variations at the demand nodes. Softwares such as EPANET 2 analyze a network using demand dependent analysis (DDA). Under pressure deficient conditions, DDA will not give satisfactory results. In such cases pressure-dependent or pressure driven analysis called as node flow analysis (NFA) is required, where outflows at the demand nodes are treated as functions of pressure using node head-flow relationships. Fuzzy analysis helps in understanding how the uncertainty in various independent parameters of water distribution network such as, nodal demands, pipe roughness values, reservoir heads, pipe diameters and so forth will affect the dependent parameters such as pipe velocities, discharges and nodal pressures. The membership functions of dependent parameters are obtained by considering membership functions of uncertain independent parameters. The Impact Table method from literature suggests a repetitive analysis by considering the monotonous relationship between dependent and independent parameters. The Impact Table method was also employed for carrying out fuzzy analysis under pressure deficient condition to obtain fuzzy membership function of nodal outflows. Optimization based methods of fuzzy analysis are more useful when relationship between dependent and independent parameters are non-monotonous.

Optimization based methods of fuzzy analysis are more useful when relationship between dependent and independent parameters are non-monotonous. Genetic algorithm is used in this study for performing fuzzy analysis on three networks by pressure dependent approach. The objective functions which are evaluated are (1) nodal pressures and (2) the actual demand at the nodes under the uncertain conditions. The membership function considered is triangular. The analysis is performed by setting up hydraulic model in the software EPANET and linking it with the MATLAB for performing optimization through an EPANET-MATLAB toolkit. Fuzzy NFA incorporates nodal outflows as additional dependent parameters. The outflows at pressure-deficient nodes are also observed to change monotonically as a result of changing the nodal demands and pipe roughness coefficients. Using this observation of monotonic change, the fuzzy analysis methodology of Gupta and Bhave (2007) is extended to obtain a membership function of available nodal flows under pressure-deficient conditions.The fuzzy NFA procedure has the following steps:Step 1. Carry out a NFA of the network considering normal values of uncertain parameters.Step 2. Change an uncertain parameter to its maximum value keeping other uncertain parameters at their normal value; and obtain the values of dependent parameter. Note the impact of changing an uncertain parameter on the dependent parameter, i.e. whether increasing, decreasing or none. Step 2 is to be repeated for all uncertain parameters.Step 3. To determine the maximum (or minimum) value of the dependent parameters, take an appropriate extreme value of an uncertain parameter based on its impact and carry out the analysis. Step 3 can be repeated for all dependent parameters and for different α-cuts.For validation, two networks were selected from previous researches, and the networks were analyzed with the developed model and WaterNetGen software, and finally the results were compared. Then paid to hydraulic analysis of the water distribution network of Jangal city under uncertain conditions and lack of pressure as a case study.

Fuzzy analysis is the approach adopted in this study to quantify thevagueness of a water distribution network. An optimization model is employed to find out theextremities in the responses of the system, as the relationship between independent and dependentvariable are not necessarily monotonous. The hydraulic system is analyzed by node flow analysis totake care of the head-demand relationships under pressure deficient conditions. As most of the hydraulic simulation software perform analysis considering that demands are met at all the nodes, the results will vary when there is a pressure deficiency in the network. This assessment of network can be incorporated into the better designing of a WDN. The obtained results showed that the proposed model can perform hydraulic simulation well in the condition of lack of pressure. This study has considered only the uncertainties in the nodal demands, but it can be further extended to any input parameter just by considering them as variables in the algorithm and extreme responses can be obtained.

Due to the increase in population and the need for water supply, preservation and protection of surface water and groundwater resources has been considered by governments. One of the pollutant sources in rivers is entering salinity from groundwater into the river, that in this research is considered as distributed (non-point) sources. The goal is to identify the salinity intensity, location and length of sources by measuring the temporal distribution of concentration in one observation point. For this purpose, the inverse solution of advection-dispersion equation in the river was employed using the simulation-optimization approach. MIKE11 numerical model was used to simulate flow and transfer of salinity in the river, and genetic algorithm was employed for optimization. In the proposed model, considering only one observation point with some measured intensity data for recovering several sources, unknown location and length of the sources, in addition to their intensities is the most significant advantage of the present study. The model verified by using hypothetical examples, 40 km section of the Karun River and also by applying five and 15 percent noise to the observation data. The results confirm the ability of the model to recover the specifications of several distributed sources using only one observation point. With five percent of noise in the observation data, all three specifications of sources can be recovered with the desired accuracy. While at 15 percent of noise, the accuracy of the model in recovering the location and length of sources was decreased. Also, to recover the specifications of each source, employing only three points of the measured data in the ascending part are sufficient.

Water is a critical input for agricultural production and plays an important role in the sustainable development of the agricultural sector and the economic development of other sectors. Water shortage is one of the major challenges that most countries in the world are struggling with. The great importance of determining the optimal cropping pattern of crops and irrigation allocation planning in the water shortage conditions prevailing in Iran's watersheds is not hidden from anyone. This study aimed to optimize water allocation and determine the optimal cropping pattern of crops in five cities of the Sistan region under different management scenarios.

In this study, a model with a two-level planning approach, the Stackelberg game framework, and a genetic algorithm, was developed to optimize the water allocation between irrigated areas and crops, as well as to determine the optimal cropping area in 5 cities of Sistan region (including Hamon, Hirmand, Zahak, Nimroz and Zabol) to confront different water conditions. Selected products include wheat, barley, onion, melon, watermelon and alfalfa. The problem was solved in the form of nine scenarios, including three scenarios of irrigation efficiency, three scenarios of climatic conditions, and three scenarios of low irrigation conditions, and compared with the baseline scenario.

For water allocation among the studied regions in the baseline scenario, the most and least water was allocated to Zahak and Hirmand areas, respectively. Among the studied crops, the most water was allocated to the melon due to its low water requirement and high economic value compared to other crops, and the least amount of water was allocated to the alfalfa due to its high water requirement; also the melon with 13503.5 ha had the highest cropping area and alfalfa with 4.85 ha had the lowest cropping area in a total of five cities. The total cropping area obtained by the model was 18240.32 ha. The value of the Gini coefficient was 0.0053, which is small and close to zero, indicating that the water allocation between the regions was fair. The total interest rate obtained in the baseline scenario was 5.06 ×1013, which by applying irrigation efficiency scenarios of 50 and 70 percent, the amount of profit has increased by 12% and 34%, respectively, and the cultivated area by 27% and 47% compared to the baseline scenario. And in normal and wetyear conditions, the amount of total profit has increased by 28% and 54%, respectively, and the cultivated area by 40% and 65% compared to the baseline scenario. By applying the low irrigation scenario, the interest rate decreases compared to the baseline scenario, associated with the saving of a large volume of irrigation water, which leads to an increase in the cropping area of crops with higher economic efficiency. Therefore, by applying various scenarios, the cropping pattern moves towards crops with less water requirement and higher economic value.

According to the results, more water has been allocated to crops with higher economic value and less water requirement. Therefore, if crops with less interest rate compared to their water use and crops with high water requirement are removed from the cropping pattern and crops with higher economic efficiency and less water requirement are replaced in the cropping pattern, it can be a good solution to confront water shortage conditions. The increase in irrigation efficiency increased the total interest, thus it is recommended to save water used by plants through the improvement of irrigation technology and increase irrigation efficiency. The proposed model in this study can be employed for correct and efficient planning for agriculture and water resource management in various conditions.