جستجوی مقالات مرتبط با کلیدواژه « Optimization » در نشریات گروه « آبخیزداری، بیابان، محیط زیست، مرتع »

Population growth and industrialization have led to an increase in waste and sewage production, which is always a concern for urban managers. With the increasing population, in addition to increased waste production, the need for energy also increases. With the increase in energy consumption, a renewable source must be sought to meet the country's needs. Municipal solid waste and sewage sludge have a high potential for energy production due to the high volume of organic matter in them. Therefore, in addition to waste disposal, appropriate management of waste and sewage can be used as a suitable source of energy production. In this research, simultaneous management of sewage and solid waste in Tehran was carried out. For this purpose, information was implemented on a superstructural model, and then, using Python programming language and decision nodes, decision coefficients from 0 to 100 percent with a one percent step were considered. Finally, 592 scenarios were proposed, and two scenarios were selected as the best scenarios in terms of energy perspective, with the approaches of heat recovery and electricity production. In these two scenarios, approximately two million gigajoules and one million four hundred thousand gigajoules were recovered as the highest heat and electricity from simultaneous management of sewage and solid waste in Tehran city, respectively. By implementing this model for Tehran, urban managers can use waste and sewage as a source to meet the energy needs of waste disposal systems and supply part of the city's energy needs.

Given the application of hydrological response in a watershed, various methods have been used to determine this response and the results have shown a high degree of accuracy and accuracy variability depending on the data used. By reviewing and summarizing the results of research carried out in the modeling of rainfall-runoff, particularly the time-area method, it was found that in most of these studies, the concept of watershed time concentration was used, which in most of the formulas used, the physical properties of the watershed were used and the dependence of time concentration on rainfall conditions was not studied. Therefore, this study was conducted to evaluate these methods using the kinematic wave method in the GIS environment, the HEC-1 method, and optimization methods using genetic algorithms in a V-shaped experimental watershed.

Observational data available in the V-shaped experimental watershed of the University of Illinois was used for rainfall-runoff modeling. The studied watershed had an impermeable aluminum surface and two uniform side sheets with a one-sided slope towards the channel with a constant value of 1%. In addition, a central channel with a one-sided slope towards the outlet of the watershed with a constant value of 1% was present. The roughness coefficient in this watershed was determined based on trial and error at 0.014.

After preparing the time-area histogram of the watershed using each of the mentioned methods, the corresponding outflow hydrographs of the watershed were determined. Then, the results were compared with observational data, and various components of the computational hydrographs were also examined. The results showed that the performance of the genetic algorithm in determining the peak time of the hydrograph with a 15% relative error was better than the performance of the kinematic wave and HEC-1 models. Additionally, the genetic algorithm model had the highest correlation coefficient with observational hydrographs with an average Nash-Sutcliffe Index of 0.968 and an average correlation coefficient of 0.983. Furthermore, by fitting the curve to the modeling results, an equation was obtained to determine the equilibrium time of the watershed relative to rainfall intensity, with a determination coefficient of 0.999. This equation expresses the inverse relationship between equilibrium time and rainfall intensity (with a power of 0.33), i.e., doubling the rainfall intensity reduces the equilibrium time by 20%. Finally, the coefficient of the equation determining the equilibrium time for this watershed was found to be 495.2, and for each rainfall intensity, its corresponding equilibrium time can be calculated with high accuracy.

In this study, under experimental watershed conditions, rainfall-runoff modeling was performed with three categories of events and three categories of different rainfall durations, each event category having four different rainfall intensity sizes. Corresponding hydrographs were obtained for each event by examining the computational hydrographs and comparing them with observational hydrographs, and it was found that the three models had one to two percent relative error in determining the maximum runoff, but in determining the time to reach the hydrograph peak, the kinematic wave and HEC-1 models had an average error of 44%. Finally, using the correlation and Nash-Sutcliffe coefficients, it was determined that the computational hydrographs produced by the genetic algorithm method were closer to the observational hydrographs and had a higher degree of correlation. It is worth mentioning that in this experimental watershed, the dependence of equilibrium time on rainfall intensity in impermeable conditions was confirmed. It is recommended that the effect of infiltration or changes in surface roughness and the determination of the dependence size be investigated.

Organic carbon is a crucial parameter for assessing soil fertility, productivity, and quality, particularly in dry and semi-dry lands. Rangelands cover approximately 50% of the Earth's land area and store over 33% of the global carbon reservoirs. Considering rangelands as significant terrestrial ecosystems and natural carbon reserves, understanding and quantifying carbon sequestration in these ecosystems is of great importance.

This research focused on rangelands in the southern domain of Damavand mountain. Four height groups were selected based on geographic conditions, regional plant cover, and slope. Each height group represented a different elevation range: group 1 (2504-2664 m), group 2 (2730-2896 m), group 3 (2923-3050 m), and group 4 (3119-3545 m). Soil sampling was conducted at two depths (0-15 cm and 15-30 cm) using 13 random points within each height group. Soil organic carbon and bulk density were measured, and carbon sequestration was calculated at the hectare level by multiplying organic carbon and depth. Regression equations and response surface models (RSM) were developed using design software to predict carbon sequestration and compare their accuracy.

Analysis of variance (ANOVA) showed that only sampling depth significantly affected soil carbon sequestration, while sampling height and the interaction between depth and height had no significant effect. Carbon sequestration was higher at a depth of 15-30 cm compared to 0-15 cm. Among the height groups, group 4 (3119-3545 m) exhibited the highest soil carbon sequestration, followed by group 1 (2504-2664 m). The highest carbon sequestration (6046.54 g/m2) was observed in height group 4 at a depth of 15-30 cm, while the lowest (2250 g/m2) was found in height group 2 at a depth of 0-15 cm. The quadratic model of the response surface methodology demonstrated higher accuracy (R2 = 0.7363) in predicting soil carbon sequestration compared to the linear model (R2 = 0.6014) and the less accurate regression model (R2 = 0.428). The response surface method successfully optimized the inputs, suggesting a height of 3500 meters and a specific depth to achieve a soil carbon sequestration amount of 8088.117 g/m2 with a satisfaction level of 0.976.

The response surface methodology (RSM) proved to be a valuable tool for predicting and optimizing soil carbon sequestration in rangelands, considering different sampling depths and heights. It can also be utilized for predicting various parameters in rangeland science, such as above and underground biomass volume and plant species coverage. RSM offers a novel approach for understanding and managing rangeland ecosystems.

Collected from sixty-six meteorological stations, the most recent data concerning the intensity-duration-frequency curve in Iran have been recorded in 1374. While rain gauges are required for physically obtaining such data, the collected data may be fraught with inaccuracies, considering the fact that the meteorological data of the extremely remote areas may be impossible to be gauged. Therefore, intensity-duration-frequency formulas are used to reduce the rate of such inaccuracies. While many formulas have so far been developed to be used for obtaining the required data regarding intensity-duration-frequency curves, they could only be applied to those areas whose regional rates have been estimated. This study sought to identify and optimize such formulas’ rates together with the computational algorithms in the Urmia Lake’s basin. Then, the efficiency of the mass-fine computational algorithm and the genetic algorithm was measured and compared. Finally, the influence of climate change on intensity-duration-frequency diagrams in the basin was investigated using the nearest neighbor algorithm.

Considering the precipitation intensity data collected from Western Azerbaijan Province’s Regional Water Organization, first, the method for selecting the function of the precipitation’s intensity-duration-frequency was determined. Then, the fitting methods for the different return years’ functions of the intensity-duration were presented. As for the extraction of IDF curves, the required data regarding the highest annual precipitation rates in 30-, 45-, and 60-minute durations were obtained from the statistics published by the Iranian Meteorological Organization. Then, the precipitation rate was investigated in twenty-seven meteorological stations of the Urmia Lake, and the intensity-duration-frequency curves were made accordingly. Taking the extracted curves into account, the regional coefficients of Abkhezr’s equation were measured and optimized based on the Genetic and Particles Swarm algorithms. Then, to determine the measurement accuracy, the daily precipitation rates were extracted from five sample meteorological stations in terms of 2-200 return years and millimeters per hour unit using the SMADA software. Finally, the IDF curves were made based on the optimized rates and Abkhezr formulas.

The average rates of optimizing the coefficients of the Abkhezr formula with both algorithms mentioned above were found to be the same, bearing a very close prediction. Taking into account the average intensity and continuity rates of the IDF curves in terms of an integral diagram, the study undertook to compare the measurement of pre- and post-optimization IDF curves in five sample stations. Generally, the findings of this study indicated the accuracy of the Abkhezr Formula’s rates (with an acceptable closeness) in all meteorological stations of the Urmia Lake except for the Mahabad camp. It could therefore be argued that the average optimized regional rate are hundred percent fitted with the obtained rates. Discussion and

The findings of the study suggested a high accuracy of the Abkhezr formula’s coefficients in all Urmia Lake’s meteorological stations (with acceptable closeness) except for the Mahabad camp, with the Siyah-Ceshmeh station having the best fitting rate. In this regard, the fitting rate of the coefficients in the above-mentioned stations differed merely at thousandth and ten-thousandth decimal rates. On the other hand, the pre-and post-optimization IDF curves were found to have slight differences, bearing an acceptable fitting. Moreover, the comparison of the integral curves of the IDF parameters indicates the great consistency between the results of this study and the formulas developed by Abkhezr and Bell. Furthermore, a good correlation was found between the results of this study and the research findings of Aghajani and Kerami, which were based on Sherman and Bernahu's formulas. As t and T in Bell's formula can have a local dependence in each area, such a dependence has been included in Abkheder's formula based on regional coefficients. The results also showed that the PSO algorithm performed more efficiently in terms of optimization and its intended function is closer to zero, as it measures each particle with other neighboring particles, putting it in numerous cycles. However, the sensitivity analysis revealed a very slight difference between the two algorithms used in this study, suggesting that both algorithms could be applied.

Due to the increase in the risk of floods, especially in the cities, and the emergence of human, financial, and environmental risks due to its increase, the flood zoning areas are of great importance. Therefore, in this study, flood susceptible areas in Birjand plain were tried to be zoned with the help of effective criteria. In this regard, the data-driven methods of support vector machine (SVM) and random forest (RF) were used in combination with genetic algorithm to zoning flood susceptible areas. Therefore, in order to implement and validate the mentioned models, 42 flood prone locations in the study area were extracted. In addition, 19 hydrogeological, topographical, geological and environmental criteria affecting flood susceptibility in the study area were extracted to be used to predict flood susceptibility map. Area under the curve (AUC) and a variety of other statistical indicators including coefficient of determination (R2) and Root mean square error (RMSE) were used to evaluate the performances of the models. The values of R2, RMSE and AUC obtained from the SVM-GA method were 0.9032, 0.2751 and 0.931, respectively, and the RF-GA method were 0.9823, 0.2321 and 0.914, respectively, which indicate the compatibility and The RF model is more accurate than the SVM model. The results also showed that the susceptibility of flooding in the central areas of the study area, due to lower altitude and slope angle, is higher than other areas.

Petrochemical manufacturing processes incorporate toxic and dangerous chemicals, making petrochemical wastewater treatment one of the world's most critical environmental issues today. As a result, this research presents an optimization model of soil characteristics for designing a natural filter for the wastewater treatment of Shahid Tondgoyan Petrochemical Company using the response surface method (RSM). After selecting and sampling soils in Bahmaei city of Kohgiluyeh and Boyer-Ahmad provinces, the physicochemical characteristics of the studied soils were determined, including their acidity (pH), electrical conductivity (EC), percentage of organic matter (OM), and percentage of clay (Clay). Following that, 12 small natural soil filters were designed in the laboratory, and effluent filtration tests were conducted after preparing the effluent from Tondgoyan Petrochemical Company. As response variables, four parameters were determined for the effluent from the filters: acidity (pH), total suspended solids (TSS), iron content (Fe), and manganese content (Mn). We fitted regression models between the four soil parameters (independent variables) and each of the effluent parameters using Design-Expert statistical software (response variables). The PSO algorithm was used to optimize regression models to obtain the optimal values for soil physicochemical parameters to produce the best effluent with the least amount of pollution. The results indicated that parameters such as acidity, electrical conductivity, organic matter, and a high percentage of soil clay would increase the acidity and decrease the total suspended solids, iron, and manganese in the effluent, thereby assisting in the treatment of petrochemical effluent. Additionally, the optimization algorithm determined that an EC of approximately 0.03 dS/cm, an acidity of 7.9, an organic matter percentage of 2.95, and a clay content of 50% are the optimal values for soil parameters whose effluent output parameters included neutral acidity greater than 7, TSS 90, iron 1, and manganese 2.3 mg / l. The findings may aid in the construction of a large-scale soil filter.

Today, the technology advancements and industrial developments have led large amounts of pollutants to make their way to aquatic environments. Cyanide compounds are a pollutant, found mainly in the gold and silver mines sewage, which should be refined due to high toxicity for human beings and the environment before discharging. In this study, silica magnetic nanoparticles were synthesized in the main accordance with the Stober method with a slight synthetic change and modified with bitter olive leaves ash; the aim was to assess the capability of the proposed method in the removal of the cyanide compounds from aqueous solutions. To determine the synthesized nanocomposite specifications, the following methods were used: X-ray diffraction pattern, Fourier transform infrared spectrometer, scanning electron microscopy, and parametric magnetometer. The experimental design in this study was one factor at a time and the effect of pH, the duration of contact, the amount of adsorbent, and the initial concentration of cyanide were investigated. Based on the results, up to 81.41% of the cyanide was excluded under the optimal conditions of PH at 5, absorbent amount of 750 mg /L, contact duration of 30 minutes, and initial concentration of 50 mg /L cyanide.  The results also revealed that the iso-thermic absorption models basically follow the Langmuir model with a correlation coefficient of 0.981. Conducting a traditional absorption study showed the speed of the absorption of the cyanide ions on Fe3O4/SiO2 adsorbent is better matched with a second-order kinetic model. Correspondingly, the study findings showed silica magnetic nanoparticles modified with bitter olive leaves ash are good in absorbing cyanide contaminants in aqueous solutions.

Major urban areas are being largely affected by population growth and immigration which eventually lead to unplanned constructions and unrestrained urban expansion. In the meantime, industrial areas as one of the most important sectors with great impact on the concentration of population and activities, accelerate the growth of cities and create a plethora of changes in the landscape. Ignoring regional planning during the decision-making process usually leads industrial areas to expand inappropriately and impose instability on the environment, interacting with the parent cities. In this study, the What if? decision support tool was used to assess the ecological potential, optimizing urban growth and industrial development in Gonbad Kavus Township, Golestan Province, Iran, simulating the future under different scenarios by 2041. The mapped criteria include land-use, multi-criteria evaluation (MCE) layers, population density, infrastructures, growth patterns as well as ecological and socio-economic driving factors. The results showed that 24107 hectares can best be allocated to urban development and 5362.6 hectares can best be allocated to industrial development. Furthermore, the findings remarked water resources, land use, and morphology as well as natural and anthropogenic hazards are as key driving forces in site-selecting the cities and industries. Most of the land-use changes were seen in the transition from agricultural to built-up and industrial areas. Accordingly, Fajr, Aghabad, and Baghli Marma districts were best found capable in terms of urban and industrial development criteria and infrastructures as well.

Estimation and forecasting of precipitation pattern in different parts of the world, especially in arid and semi-arid regions such as Iran is very important. In addition, various numerical methods such as artificial intelligence methods due to high accuracy and speed have the ability to simulate the phenomenon of precipitation and similar subjects. The use of these techniques plays an important role in saving time and costs in field and laboratory studies. Therefore, the application and popularity of various artificial intelligence techniques to estimate and simulate different issues such as rainfall is increasing day by day. The purpose of this study is to estimate the long-term rainfall in Rasht by a hybrid ANFIS and wavelet transform model.

In this study, long-term rainfall of Rasht city was simulated using an optimum artificial intelligence model for a 62 years period from 1956 to 2017. In other words, the wavelet transform was utilized to enhance the performance of the ANFIS model and hybrid WANFIS (Wavelet-ANFIS) model was defined. Firstly, the effective lags of time series data were detected through the autocorrelation function (ACF). Then, using the lags, eight models were developed for each ANFIS and WANFIS model. It should be noted that 42 years data was applied for training and 20 years data to test the artificial intelligence models. Next, the number of optimal membership functions of ANFIS model was selected equal to two.

results of ANFIS 1 to ANFIS 8 were evaluated. Additionally, different mother wavelets were examined to optimize the ANFIS model. This means that the demy was introduced as the best mother wavelet for increasing the performance of the ANFIS model. The comparison between ANFIS and WANFIS models signified that the wavelet transform enhanced the performance of the ANFIS model. Also, results of the hybrid WANFIS models were analyzed, indicating that WANFIS 8 was the superior model. The model estimated the rainfall with an acceptable accuracy. For instance, the R, MARE and RMSE for the superior model were computed 0.961, 0.855 and 24.510, respectively. Additionally, the values of VAF and NSC for this model were respectively estimated as 92.273 and 0.913.

Results showed that (t-1), (t-2), (t-3) and (t-12) were identified as the most influenced lags for estimation of long-term rainfall of Rasht city using the hybrid WANFIS model.

Lack of proper management of land use in a watershed, has negative impacts on the available resources. Land use optimization is one of the suitable solutions for achieving a sustainable development and reducing wasted resources. The present study was carried out in the Kasilian watershed located in the Savadkoh County to determine the most appropriate composition of land use to reduce flood occurrence and maximize benefit.

At first, all available studies including physiographic, soil science, vegetation, hydrology, socio-economic studies from Mazandaran Regional Water Company and Natural Resources and Watershed Management office were collected and completed during field visits. The costs and benefit for each land uses was obtained using a questionnaire. The amount of flood in different land uses was also obtained using HEC-HMS software and the use of information on three rainfall events in the years 1991-95 related to Sangdeh synoptic station and flood hydrograph of the same events in Valikbon hydrometric station located in the watershed outlet. The slope and soil depth  maps based on land use standard as input to objective functions and constraints land use optimization model was prepared. Multi-objective linear programming model using the simplex method in ADBASE software was written and solved.

The results showed that flood amount reduced from 0.89 m3 s-1 before optimization to 0.74 m3 s-1 after land use optimization in general and this indicates 24.49% reduction in flood amount. The benefit of different land uses in the studied watershed has also increased from 2505.102.285 million Rails per year, before land use optimization, to 14692.902 million Rails after land use optimization, and this indicates a decrease of 58.56% of benefit of the watershed after land use optimization according to the standard criteria.

The most sensitive factor is the reduction of rainfed land use and the increase of forest land uses in the Kasilian watershed, which have the greatest impact on reducing benefit and flood amount.

The first step of the implementation of water projects is recognizing the characteristics of rainfall, and awareness of the weather and climate of the region. Because of the spatial and time variations of rainfall, it requires much denser network to supervise compared to the other meteorological factors. Therefore, optimal localization of the stations is very necessary. Accordingly, monthly rainfall data of rain gauge stations were collected at the basin and adjacent areas first. The primary rain gauge network was created by considering the period with the largest number of the stations, the most statistical The first step of the implementation of water projects is recognizing the characteristics of rainfall and awareness of the weather and climate of the region. Because of the spatial and time variations of rainfall, it requires much denser network to supervise compared to the other meteorological factors. Therefore, optimal localization of the stations is very necessary. Accordingly, monthly rainfall data of rain gauge stations were collected at the basin and adjacent areas first. The primary rain gauge network was created by considering the period with the largest number of the stations, the most statistical years, and the lowest rebuilding ratio. Then all of time series data were analyzed by statistical analysis including normal and homogeneous tests and stations with errors and non-homogeneous statistics were removed or modified from the total stations. According to the results of normal tests, annual rainfall in 5 stations, due to their smaller amount of tests’ values toward their critical value, does not follow normal distribution. Also, the results of homogeneous tests indicated that 91.7% of selected stations in the basin were confirmed in term of the accuracy of the recorded rainfall data and could be used in hydrological analysis or water resources after this. With the spatial analysis of the precipitation entropy, the amount of information transmission entropy was calculated in the basin area and was considered as a criterion in determining the points with the potential of establishing a new station. The results indicate that the six stations of Shabestar, Sharafkhaneh, Zarnagh-Heris, Harzandat, Kalibar and Ghoshchi-sarab, while having low ranks in the network, are in critical condition and are the weakest stations in the basin. On the other hand, the stations of Saeedabad, Bashsizovjan, Maragheh, Khormazard and Shirin Kandy have the highest rank among other stations, and are five important studied stations in the basin that produce the most useful information in the network. In order to perform the optimization process, an objective function for the whole basin was determined and then the Firefly algorithm was used to obtain the best localization for rain gauge stations. The best localization is obtained by adding 9 stations after checking the results.

Metaheuristic algorithms have been increasingly used in different fields. The application of these algorithms for identifying and modelling natural phenomena such as flood and drought in terms of complexity and non-linear interactions can be considered as their capability in hydrology. In this paper, Symbiotic Organism Search (SOS) algorithm was first introduced, then its application in tuning fuzzy expert system, aiming to find the region of influence area of hydrometric stations in the Southern Caspian Sea Basin. This basin has regularly experienced flood events, causing human loss and properties damages every year. The outcome of this research is used to estimate floods, and subsequently, to design flood control structures. A total of 61 hydrometric stations were selected in the study area and their physical, climatic and hydrologic characteristics including area, perimeter, minimum elevation, maximum elevation, mean slope, stream length, slope of main stream, equivalent rectangle length, equivalent rectangle width, form factor, shape coefficient, Gravelious factor, round coefficient, and mean annual precipitation were determined. Results indicated that out of 16 parameters, area, mean elevation, form factor, Gravelious factor, and mean annual rainfall, were the most significant parameters in relation to flood by employing the SOS. These variables were used as the input variables into the fuzzy system and SOS algorithm to tune the fuzzy system. Finally, the efficiency of the SOS algorithm was evaluated using the linear torque heterogeneity statistic. Therefore, 61 influence areas were determined that show homogenous areas in 61 watersheds. Results indicated the performance of SOS in determining region of influence of the sub-basins in the study area. In addition, the geographical vicinity is not a suitable criterion for finding homogenous areas.

For suitable programming and management of water resources, access to perfect information from the discharge at the watershed outlet is essential. In most watersheds, the hydrometric station is not available; then, different models are used to simulate the discharge within watersheds without data. The selection of preferred model for rainfall- runoff simulation depends to the purpose of modeling and available data. In this research the conceptual rainfall- runoff models, SimHyd and AWBM and neural network models, MLP and RBF (by regard the less need to measured data) were used to model of rainfall- runoff process in Bar-Aryeh watershed of Nishabur. The length of data was 30 years (1983-2012) and the length of calibration and validation periods was 5 and 7 years, respectively. RRL and SPSS programs software were used for simulation of runoff. Nash - Sutcliff (ENS), coefficient of determination (R2), the root mean square error (RMSE) used to evaluate the models. Results showed that hydrological models simulate rainfall- runoff process in Bar Aryeh watershed of Neyshabur better than neural network models. Between mentioned models, the SimHyd with ENS, R2 and RMSE equal to 0.632, 0.8 and 0.02 respectively in the calibration period and 0.541, 0.74 and 0.08 in the validation period has better performance than other models which used in this research. The results showed that the Rosenbrock's search optimizer for the hydrological models and the function of tangent hyperbolic for the neural network models have more accurate operations than other optimizers. In addition, used models simulate the minimum and average values of the flow with an acceptable accuracy but the simulation of maximum values did not do well. Because these models do not regard the type and intensity of precipitation, lag time from snowmelt and concentration time of watershed.

Water availability significantly affects development of agriculture, industry and urban services. Thus, the water industry is among the fundamental industries. Yet, most countries, especially developing countries, are suffering from water deficit, water pollution and flood hazards.  These problems are more severe in Iran due to improper management of water resources. Integrated water management is a systematic process for sustainable development and water monitoring which considers socioeconomic and environmental needs. The objective of this study was to evaluate the capability of Harsin dam reservoir on Babazaid River, located 45 km east of Kermanshah, Iran for storage of surface water and groundwater recharge. Water productivity and the results of simulation were analyzed through LINGO and WEAP software using 42 years’ data. Water sustainability index was also calculated via the WEP model and Excel while taking into account the water needs of agriculture, environment and urban supply. The results indicated that the annual water deficit for urban supply, agriculture, environment and industry were 1.74, 14.2, 6.04 and 0.4%, respectively. The water need for agriculture sector in summer was considerable.

Water pollution with heavy metals is considered as a serious health problem in terms of human health risks. Therefore, it is important to remove these contaminants from polluted environments. In present study, a carbon nanocomposite was made of graphene oxide and activated carbon prepared from lignocellulosic waste and then optimum conditions for lead removal were investigated using Taguchi method and the effect of four effective factors on adsorption, including different adsorbent doses, pH values, initial concentrations and contact times were investigated at 5 levels. The iodine number of the nanocomposite was determined 920 mg/g. The maximum efficiency of lead removal by carbon nanocomposite was determined at a 0.5 g/L of adsorbent dose, pH=6, initial lead concentration of 50 mg/L and contact time=60 min. The produced nanocomposite in this study has a high potential for removal of lead from aqueous solution and can be proposed as a cheap absorbent for treatment of lead contaminated wastewater.

In recent years, deferent methods for determination of optimal pattern of using water and soil resources have been applied. Due to importance of natural resources, watershed management through land use modification and correct application of land is necessary. However, single objective linear programming is not an efficient method in this matter. In this study, multi objective method of goal programming was applied and optimal models of land use were proposed in Chelgerd watershed. Suggestive procedure is based on optimum area determination in various land uses including, dry farming, rangeland, irrigated agriculture, gardens and forest plantation and also their optimum local position. In current research, two models have been proposed. In this models, maximization of benefit and minimization of soil erosion are in first priority respectively. More over limitations of mentioned models are production resources involving water and land as well as economic and social problems. Results obtained show that proposed method is an efficient method in land use optimization and sustainable area development and can increase benefit to 97% and decrease erosion to 12%, respectively. Also, the results indicated that under optimized condition, the area allocated to dry farming lands will decrease about 33% and the area of rangelands, irrigated agricultures, gardens and forest plantation will increase about 0%, 9%, 906% and 82% in model 1 and the area allocated to dry farming lands will decrease about 54% and the area of rangelands, irrigated agricultures, gardens and forest plantation will increase about 1%, 9%, 906% and 82% in model 2 respectively.

Flood routing in river is one of important issues in water engineering projects. Hydraulic routing is common in especially in river that has branches and river that have not basin information. So as to need obtain cross section and slops in all interval of river that Muskingum helps by saving time and cost. In this paper, a Water Cycle Algorithm (WCA) is proposed for the parameter estimation of the nonlinear Muskingum model. The results of the developed model were compared with those of the other meta­heuristic algorithms including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Harmony Search Algorithm (HS) and Imperialist Competitive Algorithm (ICA). In the proposed technique, an indirect penalty function approach is imposed on the model to prevent negativity of outflows and storages. The proposed algorithm finds the global or near-global minimum regardless of the initial parameter values with fast convergence. The proposed algorithm found the best solution among 5 different methods. The results demonstrate that the proposed algorithm can be applied confidently to estimate optimal parameter values of the nonlinear Muskingum model. Moreover, this algorithm may be applicable to any continuous engineering optimization problems.

Abstract: (91 Views)
Quantitation and evaluation of infiltration models are used in soil conservation, irrigation systems, hydrology investigations, watershed and watershed management systems. The purpose of this study was to estimate the infiltration by using statistical and experimental various models in two soils of plowed and without plowed in Haraz Technology and Development center paddy fields of Mazandaran province. The infiltration value was measured by the double rings in plowed and without plowed soils and then was compared with Kostiakov, Kostiakov-Lewis, Philippe, Horton and Soil Conservation Service (SCS) models. The results showed that the final infiltration rate in plowed and not plowed soils were 0.12 and 0.21 cm/min, respectively. The comparison of the estimated infiltration from each of the models with real amounts was done by the average normal root mean square error (NRMSE). The results showed that NRMSE values in plowed and not plowed soils were 21.53 and 6.45 %, respectively. With optimization of models in Solver Excel, amount of NRMSE reduced to 2.48 and 1.9%, respectively. Also according to comparison of infiltration models in both plowed and not plowed soils, the SCS and Kostiakov-Lewis models showed better fits than other models. So that, SCS model for not plowed soil was I=0.8459t1.0417 .6985 and for plowed soil was I=0.891t1.026 .6985. In general, could be said that fitting of physical and empirical models had good accuracy on Mazandaran paddy fields. Another finding of this study was the better results in the plowed soil based on NRMSE. Therefore, this method can use in paddy fields.

Rainwater harvesting is one of the most important techniques for collecting water. Harvesting and using rainfall runoff can help with better management of water resources. The purpose of this study was investigating the economy and potential of rainwater harvesting from the rooftops in Fasa University. Daily rainfall data for 30 years (1987-2016) at the Fasa station was collected and used to create monthly statistics. After analyzing the data in terms of accuracy and homogeneity, time series data were fitted to the different functions. After selecting the best distribution function with respect to the residual sum of squares, daily rainfall on 2 return periods was calculated. Using empirical equations and available parameters, the volume of the reservoir needed to store the harvestable water was determined. Finally, using the net present value, the time of return on capital and the economics of the project were studied. The results showed that the maximum volumes of water that can be harvested in daily precipitation is 24.6 m3, occurring in February. Therefore, according to the purpose and the selected method, we need reservoirs to store 24.6 cubic meters of harvested water. Also, the results of economic analyses showed that the return on investment of 250 million Rials occurred in the 6th year. Therefore, it is suggested that this project be implemented as a technical and economic plan at Fasa University.

Well designed monitoring networks are essential for the effective management of groundwater resources but the costs of monitoring well installations and sampling can prove prohibitive. The challenge is to obtain adequate water quality and quantity information with a minimum number of wells and sampling points, a task that can be approached objectively and effectively using numerical optimization methods. Unfortunately, aquifer systems tend to be complex and monitoring can be very expensive, particularly when it requires the installation of a dedicated network of monitoring wells. In recent years, the challenge has been to design monitoring networks that are both efficient and cost effective. With regards to groundwater monitoring systems, where the challenge is to maximize the availability of good quality data while minimizing the number of sampling sites and thereby limiting costs, optimization techniques clearly have a potentially valuable application. In this paper we use a site in northern Iran to test the ability of GA, when used in combination with Kriging, in comparison with PSO to lower the cost of a monitoring network by reducing the number of monitoring wells without compromising the quality of the interpolated data. The results of the optimization showed that the number of observation wells in the Astaneh aquifer monitoring network could be reduced by 26% from 57 to 42 without a significant loss of information. The root mean square error (RMSE) for the final optimized network in GA was 0.2025 m, that in PSO 0.3222 m. A comparison of RMSE values determined using the GA algorithm with those calculated using PSO algorithm technique showed good agreement and provides strong support for more efficient GA approach.