genetic algorithm

The aim of the present study is the application of the genetic algorithm model for optimizing the cropping pattern in form of a multi-objective model in the normal and dry climates of Goharbaran area in Sari. In this regard, at first, the optimal products composition, maximum economic water productivity, minimum water consumption, minimum consumption of chemical fertilizers from different rice cultivars have been determined. At second, results were compared with the current conditions in the region.In this study, the method of genetic algorithm with respect to the conditions of different aquatic years has been used to achieve this goal. A multi-objective planning model has been used to achieve integrated economic and environmental management. The data of the present study were collected through the Jihad Agricultural Organization and Mazandaran Regional Water Company in the 2019-2020 crop year. Matlab software has also been used to estimate the resultsThe results show that the suggested pattern of the genetic algorithm in these climate conditions superior to the current model of the region and shows more appropriate achievement of study objectives. The optimal pattern in normal climate conditions will be improved in economic goal, ecological goal, and environmental goal by 16%, 3.5%, and17.5%, respectively. Also, in dry climate, the economic goal increases by 17%, the ecological goal decreases by 22% and the environmental goal decreases by 20% compared with the current cultivation pattern of the region.

In most countries, agriculture is the largest consumer of water due to its biological nature and strong dependence on nature. As a result, water management in this sector plays an essential role in determining how countries use their water resources. The present study was conducted to optimize the area under cultivation, allocate irrigation water, and maximize crops’ benefit in the Damaneh-Daran plain, Isfahan, Iran, from 2017-2030 under the RPC8.5 scenario using a genetic algorithm. Following monthly runoff estimating by the AWBM model under climate change conditions within a correlation coefficient of 75% indicated that the values of RMSE, MBE, and R of the microscale parameters measured by the Statistical Micro-Scale Model (SDSM) were equal to 8.34, 7.51 and 0.98 in the temperature parameter and -1.28, 6.28, and 0.78 in the precipitation parameter, respectively. Wheat, barley, sugar beet, and fodder corn cultivation were reduced by 2130.6, 1176.1, 112.8, and 516 hectares, respectively, by pattern optimization. The expansion of potato cultivation by 3935.5 hectares reduced water usage by 18.21 Mm3and generated a total profit of 163 million tomans for farmers between 2017 and 30.

In recent decades, increasing of population and development of technology and, consequently, intense agricultural and industrial activities have exposed groundwater resources to a variety of pollutants. Ajabshir plain, located in the southwest of East Azarbaijan province and southeast of Urmia Lake, is one of the areas that has faced groundwater contamination and needs more qualitative studies. In this study, first, the conventional drastic method was used to investigate the potential of nitrate contamination in Ajabshir plain, and then the optimization process was performed using the methods of genetic algorithm and fuzzy logic (Sugeno). The index values in the conventional drastic method were obtained from 87 to 145, as well as the drastic index values obtained by considering the weights of the genetic algorithm and optimization with fuzzy logic, from 47 to 74 and 0.01 to 0.6, respectively. According to the allergy classification the ordinary drastic is located in low, low to medium, and medium to high ranges, in which areas from the north of the plain and the north of Ajabshir city had moderate to high vulnerability index. Also, the optimized variables with genetic algorithm and fuzzy logic are in the safe zone in terms of contamination potential due to lower index values than 79. The normal drastic correlation coefficient, genetic algorithm, and fuzzy logic method with nitrate concentration were 0.2,73, 0.57, and 0.796, respectively. Therefore, the results show the superiority of the fuzzy logic method over other methods.

Wind erosion is an important cause of land degradation and desertification in arid and semi-arid regions of the world. This phenomenon occurs more severely in dry and bare soils. During wind erosion, soil particles are transported by three mechanisms known as creeping, saltation and suspension, which result in numerous on- and off-site damages. Wind erosion as a natural phenomenon, is affected by many factors. Our understanding of wind erosion is commonly constrained by the multiplicity and complexity of factors in this process. In recent years, solving pattern recognition and optimization problems with metaheuristic algorithms has received considerable attention among researchers. Genetic algorithms is a search technique inspired by the process of natural selection, which is well applied to multimodal, non-linear, and non-derivable objective functions. In the current research, for identifying the most important parameters affecting wind erosion rate, two GA-ANN and NSGA-II hybrid algorithms were developed using genetic algorithm and artificial neural networks. In order to prepare a suitable and reliable data set; after designing a grid sampling strategy, soil samples were collected from 51 study sites in the Narmashir plain, Kerman and then some soil parameters were measured. In addition, wind erosion rate was determined at each study site using a portable wind tunnel device. Based on the GA-ANN algorithm results, gravel coverage, sand, clay, aggregate stability, surface crust, moisture, and organic matter were identified as the main determinant features affecting spatial variation of wind erosion rate. However, the selected feature subset by NSGA-II algorithm included gravel coverage, sand, aggregate stability, surface crust, and moisture. The calculated error function for the GA-ANN algorithm performance was 3.58%. It was 1.70% for the NSGA-II algorithm performance. According to the results, both algorithms had acceptable performance to achieve the purpose of the present study. Therefore, the algorithms developed in this study can be applied to identify the most important parameters affecting wind erosion rate in other areas with similar challenges.

Water is one of most important factors in the growth and development of human societies, where water resource limitations have always been one of the main barriers to agricultural development as a major basis for achieving food self-sufficiency. One of the main applications of satellite imagery is its utilization in the field of water resources management and agricultural activities, in which managers can benefit from it for studying cultivation levels, crop classification, crop estimation, and agricultural crisis forecasting. Generally, overall consumption estimation, water/irrigation management, and utilization of dams’ storage capacity are among the most important research topics. This study benefits from the Sentinel-2 satellite for classifying the agricultural crops based on the multi-temporal methods. Besides, four classification methods are adopted for classifying, namely, minimum distance, maximum likelihood, fuzzy, and neural network. Due to the spectral changes of goods during the growing period, using the multi-temporal methods based on the crop calendar can play a decisive role in classifying process, such that the classification accuracy increases to 86 percent via the maximum likelihood and neural network methods. Moreover, the normalized Kappa increased to 90.5 percent, when the neural network method parameters are optimized. The results obtained from the simulation indicate that genetic algorithm is the best method for obtaining the optimal results. After selecting the optimized neural network parameters, the classification has been taken into account and observed that Alfalfa has the largest crop surface, while it requires a considerable amount of water and its demand is in a lower value.

Shabestar plain is an active agricultural area and the utilization of groundwater resources is extremely important due to the shortage of surfaces water resources. Increasing of population and technological and agricultural activities possibly causes the aquifer contamination in this area. Therefore, assessing the groundwater vulnerability of this aquifer will be very useful for development, management and land use decisions, to monitoring of the groundwater resources quality and preventing the contaminations of groundwater resources. . In this study DRASTIC and SINTACS methods were used to assess the vulnerability of the Shabestan plain aquifer. Considering that the ratings and weights of the DRASTIC and SINTACS models are somewhat expertly Wilcoxon rank-sum test (WRST) method was used to improve the ratings in both the models and in order to optimize weights, simple statistical (SS) and genetic algorithm(GA) methods were used. Finally, the optimized WRST-SS-DRASTIC, WRST-GA-DRASTIC, WRST-SS-SINTACS, WRST-GA-SINTACS models were made. In all optimization models, the detemination coefficient between the nitrate concentration and the vulnerability index was increased relative to the original model. The higher determination coefficient of the WRST-GA-SINTACS model than other optimized models represents the better performance of this optimized model in the study area.

In today's world, one of the basic human problems is securance of food need due to inputs limitation. Accordingly, cropping pattern optimization is a suitable strategy for agricultural development and human food securement. In this study, optimal cropping pattern has been determined and compared by using of ordinary non-linear programming and genetic algorithm in Goharbaran region of Sari County. Required data for this study has been collected with interview of 250 farmers during the 2014-2015 crop year. The results of this study showed that the optimal cropping pattern of non-linear genetic algorithm is superior compared to ordinary non-linear programming model because of more variety and increasing profit by 2 percent and reducing risk by about 22 percent. Since the proposed cropping pattern of genetic algorithm causes to increase farmers' gross margin compared to the ordinary nonlinear programming, so the government should encourage and support farmers on the application of the results of such models.

PM2.5, (PM10) and visibility are known as three important parameters in researches connected to the tropospheric aerosols and dusts, so that the air pollution is related to those at the specific time. The aim of this study is analyzing the relationship between PM2.5, PM10 and visibility whit using evolutional Genetic Algorithm. The area’s case study was Yazd city as representative of central of Iran. PM2.5s data and also visibilities data whit separation of 05, 06, 07 and 09 synoptic conditions, for 5 years (2010-2015) from Yazd Meteorology Organization; and PM10 data from air pollution control stations connected to Yazd Environment Organization has been catches. To reach mentioned mathematic relations, liner regression equation, and Weibull, Rational, Power, Polynomial, Exponential, Liner, Fourier and Gaussian functions has been comparison; which based on relative and Sum Square Error and also coefficient correlation, Polynomial function selects as the best fitness function. The results of this research were four equation based on liner model of Polynomial function in 95% confidence level, for estimating the relations between PM2.5, PM10 and visibility in general; and also when to happen 05, 06 and 07 synoptic conditions.

In this study, the use of genetic optimization algorithm (GA),Particle Swarm(PSO), the ant colony for continuum (ACOR)and differential evolution (DE),to develop and improve the performance of ANFIS were investigated. the monthly maximum temperatures in Isfahan during the period of 64 years (1951-2014), was simulated and analyzed. At first in a sensitivity analysis, the best entries for each prediction period (1 month, 1, 2 and 3 years) were selected. Then, the maximum temperature hybrid models by ANFIS-GA,ANFIS-PSO,ANFIS-DE,ANFIS-ACOR and ANFIS were examined. The performance of each model with regard to R2, RMSE and MAE were evaluated. The results showed that the ANFIS-GA, as the most appropriate model, increased ANFIS performance in R2 to by 0.06, 0.07, 0.08 and 0.12 and RMSE by 0.09, 0.09, 0.16 and 0.1, respectively, in 1 month and 1, 2 and 3 year. After, ANFIS-DE and ANFIS-PSO, respectively, had the best forecasting accuracy. On the other hand, ANFIS showed highest error and lowest R2, as the weakest model. The results showed that the proposed models, which use global search techniques and avoid being trapped in local optimum, could improve the performance of ANFIS favorably.Therefore, these models can be used in other areas related to hydrology and water resources.

Current research presents a systematical water allocation scheme for Bukan reservoir located in the northwest of Iran. In this scheme, the agricultural demands reduced by application of some fallow to satisfy other sector demands, when the system faced with a water scarcity. For this, the monthly water deficit ratio calculated for the historical period of 1995- 2013. To determining water deficit thresholds in the reservoir it classified to five groups by k-means clustering method. For determining the optimized amount of fallow genetic algorithm used to optimize the operation of the reservoir for each crop year from 2007-2013. For assessment of the irrigation efficiency also consideration of the environmental demand effect to the fallow amount, six scenarios are applied. Results revealed that by increasing of irrigation efficiency it needs less fallow. Applying optimized fallow leads to deliver downstream demands at an acceptable level. So increasing irrigation efficiency should be as a priority of water managers.

Prediction the river flow discharge values are important in the surface water resources management. Find an appropriate model to accurately predictionof this parameter is one of the most important ways to simulation and prediction. In this study three ANFIS, SVM and GP models were evaluated and compared to modeling the monthly flow discharge of Nazloochi River in Tapik hydrometric station that located in western of Urmia Lake based on precipitation of river basin. All the methods listed in M1 to M5 data flow patterns with a delay of 1 to 5 M6 to M10 and patterns of precipitation and discharge data combined with delays of one to five months were studied.To investigate the value of modeling’s error three coefficient of determination, root mean square error and effectiveness criteria tests were used. The results of evaluation the accuracy and error values of models indicated that the combined pattern has better results only in SVM model and in GP and ANFIS models the ones series patterns presented the better results. Among the three studied models, ANFIS model with 4 and 5 delays input patterns presented the best results. Overall the results indicated that with adoption of ANFIS model to modeling the monthly river flow in Nazloochai River, error values were decreased about 23 and 3 percentages respectively in GP and SVM models and accuracy of modeling compared to GP and SVM models were increased about 10 and 4 percent respectively.

Flood routing in rivers is a mathematical procedure to determine flow hydrograph at a point in a river. One of routing methods is based on solution of Saint-Venant equations for unsteady flows. Because this method is very complex and need to more information of river, other methods with simple calculations and reasonably accurate results have been extended and give generally satisfactory results for hydrologist researchers and need to lower information of river. Muskingum model is one of these methods that accuracy in evaluation of its parameters effects on predicting flood hydrograph, especially peak rate of flow. As far as genetic algorithm is an appropriate solution to determine optimized nonlinear Muskingum coefficients, in this study we used this method to determine optimized coefficients in MATLAB and for a wide range of hydrographs getting from HEC-RAS software. Then the nonlinear Muskingum coefficients were presented as functions of river characteristics and inflow hydrograph. The model has been developed using the functions and solving of differential continuty equation with Rung-Kutta order 4. To determine the accuracy of the model, measured hydrographs of 5 floods in a reach, were compared to hydrographs computed by the model and HEC-RAS software and the results were analyzed using RMSE factor and correlation coefficient. Results indicate that there was no significant diference between the model and HEC-RAS .