Irrigation
E. Rezaei; M. Montaseri; H. Rezaei
Abstract
Introduction: Prioritization of optimal water allocation of surface flow storage dams for different applications (drinking, agriculture, industry, environment, etc.) in arid and semi-arid regions such as Iran due to the range of changes, high flow uncertainty Reservoir inlets, and the occurrence of intermittent ...
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Introduction: Prioritization of optimal water allocation of surface flow storage dams for different applications (drinking, agriculture, industry, environment, etc.) in arid and semi-arid regions such as Iran due to the range of changes, high flow uncertainty Reservoir inlets, and the occurrence of intermittent droughts are of great importance. For this purpose, the Fuzzy Hierarchy Process (FAHP) is proposed and used as a suitable formulation method in prioritizing water allocation in the water resources system. Therefore, in this study, prioritization of water allocation for different purposes of Shahrchai reservoir dam located upstream of Urmia metropolis has been done in a field study using fuzzy hierarchical method.Materials and Methods: A fuzzy hierarchical process based on quantitative and qualitative effective factors has been developed. In the first stage, the problem structure was designed by determining the priority of water allocation of users, criteria, sub-criteria, and other factors. Then the decision-making hierarchy based on the problem structure (purpose, criteria, sub-criteria, factors, and options in the first to fifth levels, respectively) was defined. In the mentioned prioritization structure, the goal was determined at the first level, ie the optimal or appropriate allocation of Shahrchay reservoir dam water for different operators, and at the second level, three economic, social and environmental criteria were considered as the main criteria. At the third level, " cultivation area and gross income" and "employment and population" were considered as sub-criteria of two economic and social criteria, respectively. The main beneficiaries, namely agriculture, urban drinking, recreation and tourism, industry, environmental needs of Lake Urmia and groundwater fourth level (options) have formed the problem structure. At the next step, based on the field data or questionnaires, criteria, sub-criteria, and factors were compared in pairs using the proposed linguistic and fuzzy comparisons, and the priority of water consumption over each criterion or sub-criterion or factor were compared based on fuzzy triangular numbers. The weights were determined and ranked each using the Chang development method. At the third stage of the final ranking, the priority of water allocation was determined based on the final weight of criteria or priorities at the previous stage and the superior option was determined. Finally, a sensitivity analysis of the weight change of the criteria and the decision-making process of the problem has been performed.Results and Discussion: A decision model based on a fuzzy approach is presented to rank the different options using Shahrchay dam water. For this purpose, firstly, using the opinions of experts and researchers, the results of a questionnaire, criteria and sub-criteria and important options in allocating water to Shahrchai Dam were determined. Secondly, using Chang's development analysis, different options were evaluated based on the mentioned criteria, sub-criteria, and factors. From a scientific point of view, because the questionnaires were presented to experts, the economic criterion is a high priority, so it is possible to attach great importance to the general conclusion about the criteria in economic attitudes and related issues. In addition, the allocation of water to the urban drinking sector with a weight of 0.33 was as the top priority, agriculture, Lake Urmia, industry, groundwater, and recreation were in the next priorities, respectively. Therefore, economic criteria and drinking water supply were recognized as the main objectives of planning and managing water resources in the metropolis of Urmia. The drinking sector is a vital factor for the survival of a community and because the drinking water of Urmia city is supplied through Shahrchai dam, so the allocation of water to this sector should be considered as the top priority. The agricultural sector was also given the second priority with less importance. The supply of water to this sector has a significant direct effect on the economy of the agricultural sector and indirectly on the entire economy of the region, which indicates the importance of the agricultural sector in the economy, living conditions of the region and the allocation of water to this sector. Comparing agricultural and industrial activities in Shahrchai catchment area, the most activity in the region is agriculture and industry is in a lower priority, which is also shown by the hierarchical results. Since Shahrchai River is one of the suppliers of water to Lake Urmia, the allocation of water to this section improves the condition of the lake and, consequently, it improves the environmental, economic, and social conditions of the region. The results also indicate the importance of Lake Urmia in relation to industry and its higher status indicates the attention of officials to the drying crisis of the Lake Urmia.
m ahmadian; M. Montaseri
Abstract
Introduction: In recent decades, with increasing the world population and demand for fresh water for various applications (drinking, agriculture and industry), planning, management and optimal utilization of surface water reservoirs, especially in arid and semi-arid regions, have become the most serious ...
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Introduction: In recent decades, with increasing the world population and demand for fresh water for various applications (drinking, agriculture and industry), planning, management and optimal utilization of surface water reservoirs, especially in arid and semi-arid regions, have become the most serious challenges faced by researchers and water industry professionals in many parts of the world. In surface water reservoirs, uncontrolled flow is stored in wet periods for use in low flow periods. Therefore, surface water storage dams are created to control and regulate the flow of rivers in order to meet demand for different uses at a certain level of performance indices. During the process of storing water in the reservoirs, the uncontrolled flows of the input into the reservoir are in three ways: yield or output adjusted to meet demand for various uses, infiltration loss and evaporation from the surface of the lake and spill of excess water in a reservoir that is part of an uncontrollable flow. The proposed methods of storage-yield-performance of the storage system are classified into two main groups, simulation and optimization methods, which are widely used to analyze the reservoirs system for storing surface water. Among two final methods of simulation i.e. the behavior analysis method and the modified Sequent Peak Algorithm (SPA) method, all the actual conditions governing the system of storage reservoirs, including control of indices of reliability and vulnerability in the storage-yield-performance, are required to apply SPA. The basic SPA simulation method has been proposed as a computational method for the mass curve, and major improvements have been made to increase its functionality and efficiency at the late 20th century. The first amendments to apply the effects of evaporation losses and performance indices; time-based reliability and vulnerability, were carried out by Lele (1987). Then, Montaseri (1999) developed the SPA method for the system of multiple storage reservoirs and used non-linear or real area-volume relationship for applying losses caused by evaporation.
Materials and Methods: Stochastic models provide the possibility of generating successive hydrological time series (such as rainfall and flow) that are likely to occur in the future. On the other hand, the analysis of long-term behavior of various water resources systems, especially the storage system, depends on the availability of expected river flow time series in the years to come. Therefore, the use of stochastic models and the production of artificial data are absolutely necessary for the accurate evaluation of the design, operation and optimal management of the storage system and the elaboration of their long-term behavior. For this purpose, using a single distributed stochastic model, 1000 series of annual and monthly flows of input into the storage reservoir were generated and then the series of monthly flows generated to simulate the storage reservoir system using the SPA-I method and the reservoir performance indices (time reliability, resiliency and vulnerability) were also used for single reservoir system.
Results and Discussion: The results show that combining two stochastic AR(1) and Valencia-Schaake models had very good performance in preserving statistical data of historical data at two monthly and annual levels. This is the advantage and necessity of using the stochastic distributions model relative to other stochastic models such as Thomas-Fiering and ARMA in analyzing the storage reservoirs systems. The behavior of the reservoir system or the critical period in addition to demand, depends on system performance indices and decreases the critical period by decreasing time-based reliability or increasing the vulnerability factor. The results also indicate nonlinear (exponential) changes in the critical period and demand at a certain level of performance indices. Moreover, evaporation loss changes for demand and a certain level of performance indices have a concave shape, with a reversing point consistent with the largest within-year storage system. With a decrease/ an increase demand and volume of storage, the amount of evaporation losses increased exponentially and accounted for a considerable percentage of the reservoir's storage capacity.
Conclusion: The results revealed that volume of storage in addition to demand is a function of evapotranspiration losses and time-based reliability and vulnerability indices and follows an exponential relation for demand. In addition, in all three variants of the modified SPAs (SPA-I, SPA-II, and SPA-III), two performance indices of the reservoir, namely time-based reliability and vulnerability, are controllable in analysis, and the storage system analysis is performed for specified values or mentioned indices. Also, in the SPA-II and SPA-III methods, it is possible to use a nonlinear or a real are-volume relationship to estimate the loss of evapotranspiration in the storage system. Control of two performance indices of the reservoir and the application of real or nonlinear area-volume relationship in the analysis of reservoir system reservoir are important advantages of the above methods to the behavior analysis method.
M. Montaseri; B. Amirataee; H. Rezaei
Abstract
Introduction: Drought is a natural phenomenon and was described when precipitation is less than expected. Since the precipitation amounts in terms of spatial and temporal characteristics are different from one region to another, so this phenomenon is known as a multivariate phenomenon. This phenomenon ...
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Introduction: Drought is a natural phenomenon and was described when precipitation is less than expected. Since the precipitation amounts in terms of spatial and temporal characteristics are different from one region to another, so this phenomenon is known as a multivariate phenomenon. This phenomenon often characterized by different variables such as drought duration, severity, intensity and spatial extent. Although site specific analysis can provide useful information on drought occurrences in a limited area, but these results have a fundamental uncertainty to drought risk assessment in a large region. Therefore regional drought analysis, provides a more comprehensive assessment in each region, and is essential for short and long term management of water resources. Meanwhile, the copula functions has been developed as a new advanced technique for modeling the two or multivariate joint probability distribution in different fields such as financial, hydrology, water resources and risk management. So, in this research, regional analysis of drought severity and percent of drought area were performed using the copula functions in Lake Urmia basin, as one of the Iran's drought-prone basin. Such study with emphasis on bivariate analysis of drought severity and drought areal extend were conducted for the first time in the study area. The main objectives of this study are: 1) Modeling drought characteristics in Lake Urmia basin, 2) Evaluation of copula functions in modeling the structure of the region's drought characteristics, and 3) Develop the Severity-Area-Frequency curve using the appropriate copula.
Materials and Methods: Copula is the stochastic model and based on probability. In other words, copulas are function for modeling the two or multivariate random variables. Copulas can be easily coupled the marginal distributions to multiple distributions. There are many parametric copula families available, that seven copula functions such as archimedean (Clayton, Frank, Gumbel and Joe), extreme value (Galambos), elliptical (Normal) and others (Plackett) were used. The SPI-1 was determined at each station and then, the whole area was divided into small grids with cell size of 2000×2000. Distances between the grid centers with all the selected stations were calculated with a programming code. Finally, the SPI values in each grid were calculated using IDW method. The severity and percentage of drought area variables were determined and used for regional drought modeling in the study area based on drought threshold equal to zero. After determining the best statistical distribution of two variables, the appropriate copula function was conducted based on different goodness of fit tests. Finally, the Severity-Area-Frequency curve for the study area was developed based on the appropriate copula function and conditional return periods.
Results and Discussion: The correlation between the two variables of percentage of drought area and severity was assessed using different graphical (Kendall plot and Chi plot) and statistical tests (Spearman rand order correlation and Kendal tau). The results showed a positive correlation between the drought severity and percentage of drought area variables. Based on Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) and graphical test, the Lognormal and Beta probability distributions were select as a best fit distribution of severity and percentage of area under drought, respectively. Finally, the Frank copula among other type of copulas was selected as an appropriate copula for modeling joint drought severity and percentage of area under drought for the study area based on Maximum log likelihood, AIC, BIC and RMSE criteria. The S-A-F curve was developed using conditional return periods based on Frank copula. According to S-A-F curve, it can be seen that increase in the percentage of area under drought in the study area led to increase in drought severity and vice versa. For example, drought severity with return period of 20 years and drought with 20 percent areal extend is obtained equal to 0.37.
Conclusions: Copula functions are of great importance in the analysis of drought, due to preserve correlation between variables and not have any limitation to have a same marginal distribution in long-term prediction of drought events. In this study, using best fit copula (Frank copula) and conditional return periods, the relationships between drought severity and percent of area under drought for the study area named S-A-F curve were developed. These curves can be useful for planning and management of drought in the region. Drought risk assessment based on the results of this study can be high priorities for drought monitoring in large areas.
Alireza Moghaddam; Majid Montaseri; Hossein Rezaei
Abstract
Introduction: The reservoir operation is a multi-objective optimization problem with large-scale which consider reliability and the needs of hydrology, energy, agriculture and the environment. There were not the any algorithms with this ability which consider all the above-mentioned demands until now. ...
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Introduction: The reservoir operation is a multi-objective optimization problem with large-scale which consider reliability and the needs of hydrology, energy, agriculture and the environment. There were not the any algorithms with this ability which consider all the above-mentioned demands until now. Almost the existing algorithms usually solve a simple form of the problem for their limitations. In the recent decay the application of meta-heuristic algorithms were introduced into the water resources problem to overcome on some complexity, such as non-linear, non-convex and description of these problems which limited the mathematical optimization methods. In this paper presented a Simple Modified Particle Swarm Optimization Algorithm (SMPSO) with applying a new factor in Particle Swarm Optimization (PSO) algorithm. Then a new suggested hybrid method which called HGAPSO developed based on combining with Genetic algorithm (GA). In the end, the performance of GA, MPSO and HGAPSO algorithms on the reservoir operation problem is investigated with considering water supplying as objective function in a period of 60 months according to inflow data.
Materials and Methods: The GA is one of the newer programming methods which use of the theory of evolution and survival in biology and genetics principles. GA has been developed as an effective method in optimization problems which doesn’t have the limitation of classical methods. The SMPSO algorithm is the member of swarm intelligence methods that a solution is a population of birds which know as a particle. In this collection, the birds have the individual artificial intelligence and develop the social behavior and their coordinate movement toward a specific destination. The goal of this process is the communication between individual intelligence with social interaction. The new modify factor in SMPSO makes to improve the speed of convergence in optimal answer. The HGAPSO is a suggested combination of GA and SMPSO to remove the limitation of GA and SMPSO. In this paper the initial population which caused randomly in all metha-heuristic algorithms consider fixing for the three mentioned algorithms because the elimination of random effect in initial population may make increase or decrease the convergence speed. The objective function is the minimum sum of the difference between the downstream demand reservoir and system release in the period time. Also the constrains problem is continuity equation, minimum and maximum of reservoir storage and system release.
Results and Discussion: The performance of GA, SMPSO and HGAPSO evaluated based on the objective function for Dez reservoir in the south east of Iran. In this study the programming of GA, SMPSO and HGAPSO was written in Matlab software and then was run for the time period with a maximum of 400 iterations. The minimum of the objective function for GA, SMPSO and HGAPSO was obtained 1.19, 1.05 and 0.9 respectively, and the maximum of objective function was calculated 1.66, 1.26 and 1.10 respectively. The results showed that the minimum of the objective function by HGAPSO was estimated 32 and 16 percent lower than the counts which calculated by GA and SMPSO. The standard deviation of SMPSO and HGAPSO were near to each other and less than GA which shows the diversity between solutions for SMPSO and HGAPSO are much less than GA. Also the HGAPSO had the better performance rather than previous method in terms of minimum, maximum, average and standard deviation. The convergence speed of HGAPSO for finding the optimal solution is much faster of GA and SMPSO. The difference graphs between system release and monthly demand in HGAPSO is much less than GA and SMPSO. Also the storage calculated in HGAPSO and SMPSO is highly close to each other but in GA method the storage calculated more in the first and second years.
Conclusions: The convergence speed in finding the optimal solution in SMPSO in more than GA but in other hand the probability of caughting in local optima for SMPSO is great whereas GA can make the diverse optimal solutions. For this reason, in this paper was trying to improve the performance of the GA and SMPSO and remove their disadvantage based on combining them and presenting a new hybrid method. The results showed the HGAPSO method which presented in this paper to use without any complexity and additional operator to GA and SMPSO has the ability to use for reservoir operation with large-scale. In addition it is suggested which the HGAPSO apply to other water resources engineering problems.
majid montaseri; Negar Rasouli Majd; Javad Behmanesh; Hossein Rezaei
Abstract
Introduction: The water footprint index as a complete indicator represents the actual used water in agriculture based on the climate condition, the amount of crop production, the people consumption pattern, the agriculture practices and water efficiency in any region. The water footprint in agricultural ...
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Introduction: The water footprint index as a complete indicator represents the actual used water in agriculture based on the climate condition, the amount of crop production, the people consumption pattern, the agriculture practices and water efficiency in any region. The water footprint in agricultural products is divided to three components, including green, blue and gray water footprint. Green water footprint is rainwater stored in soil profile and on vegetation. Blue water refers to water in rivers, lakes and aquifers which is used for irrigation purposes. Gray water footprint refers to define as the volume of contaminated water. The water footprint in arid and semiarid regions with high water requirement for plants and limited fresh water resources has considerable importance and key role in the planning and utilization of limited water resources in these regions. On the other hand, increasing the temperature and decreasing the rainfall due to climate change, are two agents which affect arid and semiarid regions. Therefore, in this research the water footprint of agriculturalcrop production in Urmia Lake basin, with application of climate change for planning, stable operating and crop pattern optimizing, was evaluated to reduce agricultural water consumption and help supplying water rights of Urmia Lake.
Materials and Methods:Urmia Lake basin, as one of the main sextet basins in Iran, is located in the North West of Iran and includes large sections of West Azerbaijan, East Azerbaijan and Kurdistan areas. Thirteen major rivers are responsible to drain surface streams in Urmia Lake basin and these rivers after supplying agriculture and drinking water and residential areas in the flow path, are evacuated to the Lake. Today because of non-observance of sustainable development concept, increasing water use in different parts and climate change phenomena in Urmia Lake basin the hydrologic balance was perturbed, and Urmia Lake has been lost 90% of its volume and has a critical condition. Therefore, planning, managing and optimizing utilization of water resources in the basin have a high research priority and this requires the concentration on the consumption of water resources. In this study five major products including, wheat, sugar beet, tomato, alfalfa and corn, were studied. For this purpose, seven synoptic meteorological station data including,Salmas, Urmia, Mahabad, Takab, Tabriz, Maragheh and Sarab were used to calibrate the downscaling atmospheric-ocean general circulation model LARS.WG5 and forecast meteorological data in the future periods time (2011-2030) and (2046-2065) with the A2 scenario.The reason to selectA2 scenario was the most critical situation for the mentioned scenario. Then the obtained data were used to estimate the water requirement and water footprint of mentioned plants separately blue and green water footprint in the future periods.
Results and Discussion:The resultsof themeteorological data prediction showed thatall synoptic stations except for Tabriz station the average annual predicted rainfallvalues had the deviationfromhistoricalvalues.The mentioneddeviation in the south (Tekab, Mahabad) and West (Urmia, Salmas) ofUrmia lake basin will showincreaseanddecrease in theannual rainfallin the future, respectively.Moreover,the average annual of predicted temperature values for all studied stations showed that the temperature will increase about1°Cduring2011-2030 period and 2°C during 2046-2065 period. Potential evapotranspiration, as another important meteorological parameter has essentialrole in the estimation of crop water requirements which will be slightly affected by climate change phenomena and it will increase in the summer. The results of agricultural products water footprint show that the maximum amount of green water footprint in all studied stations was related to wheat and alfalfa, and this water footprint depend on the time and growth period. For corn, tomatoandsugar beetproducts the ratio of blue and green water footprint is greater 9. By comparing the water footprint of products it can be seen that in Urmia, Salmas and Tekab stations water footprint is decreased with decreasing rainfall and this decrease during 2065 – 2045 periods is higher than 2030 – 2011 periods.
Conclusions: According to the results, annual precipitation in southern and western regions of the Lake Urmia basin will be increased and decreased, respectively in the future periods. However, increasing approximately one Celsius degree in temperature is expected for each of the periods all over the basin. In addition,the results showed that the amount of potential evapotranspiration will be increased in the warm months (June to September) in the future periods, and agricultural water consumption pattern will be changed affected by evapotranspiration variations. In the future periods, the blue and green water footprint of most agricultural products will be increased and decreased, respectively.
Majid Montaseri; Babak Amirataee; Keyvan Khalili
Abstract
Introduction: Droughts are natural extreme phenomena, which frequently occur around the world. This phenomenon can occur in any region, but its effects will be more severe in arid and semi-arid regions. Several studies have highlighted the increasing of droughts trend around the world. The majority of ...
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Introduction: Droughts are natural extreme phenomena, which frequently occur around the world. This phenomenon can occur in any region, but its effects will be more severe in arid and semi-arid regions. Several studies have highlighted the increasing of droughts trend around the world. The majority of studies in assessing the trend of time series are based on basic Mann-Kendall or Spearman's methods and no serious attention has been paid to the impact of autocorrelation coefficient on time series. However, limited numbers of studies have included the lag-1 autocorrelation coefficient and its impacts on the time series trend. The aim of this study was to investigate the trend of dry and wet periods in northwest of Iran using Mann-Kendall trend test with removing all significant autocorrelations coefficients based on SPI and RAI drought indices.
Materials and Methods: Study area has a region of 334,000 square kilometers, with wet, arid and semiarid climate, located in the northwest of Iran. The rainfall data were collected from 39 synoptic stations with average rainfall of 146 mm as the minimum of Gom station, and the highest annual rainfall of 1687 mm, in the Bandaranzali station. In this study, Standardized Precipitation Index (SPI) and Rainfall Anomaly Index (RAI) were used for trend analysis of dry and wet periods. SPI was developed by McKee et al. in 1993 to determine and monitor droughts. This index is able to determine the wet and dry situations for a specific time scale for each location using rainfall data. RAI index was developed by Van Rooy in 1965 to calculate the deviation of rainfall from the normal amount of rainfall and it evaluates monthly or annual rainfall on a linear scale resulting from a data series. Then, correlation coefficients of time series of these drought indices with different lags were determined for check the dependence or independence of the SPI and RAI values. Finally, based on dependence or independence of the time series values, trend analysis of wet and dry periods was conducted in different stations using one of the basic or modified Mann-Kendall tests. Also, the magnitude of the trends was derived from the Theil- Sen’s slope estimator.
Results and Discussion: Time series of SPI and RAI drought indices for a given annual rainfall as an example for three stations of Marivan, Gom and Maku show that during 1991 to 1994 and from 2002 to 2007 are in wet period and during 1987 to 1990 and 1998 to 2001 are in the dry period. It is clearly show that, dry and wet periods in RAI index are more severe than SPI. Comparison the correlation between Lag-1 autocorrelation coefficients values of SPI and RAI time series and Lag-1 autocorrelation coefficients of annual rainfall data indicate that these correlations are high and about 0.97 and 0.99, respectively. This difference is due to the different classification of SPI and RAI drought indices. The results of trend analysis indicate a decreasing trend in most of stations. Also, Mann-Kendall statistic has been declining while eliminating the effect of all significant correlation coefficients of dry and wet periods. This result in both SPI and RAI indices are similar and have a high correlation with R = 0.99. According to results, west of the study area have a significant decreasing (negative) trend. The spatial distribution of dry and wet periods showed that the difference between Mann-Kendall statistics of SPI and RAI indices is minimal. Also, The results show that, the slope of the trend line based on the SPI and RAI drought indices is negative in most of stations and correlation between these two indices in determining the slope of the trend line is high. But, this correlation compared with the trend statistics of SPI and RAI time series is less.
Conclusions: In this study, first the time series of SPI and RAI time series based on annual precipitation and common quantitative classification of mentioned two drought indices were determined. Then, trends of dry and wet periods of selected stations in northwest of Iran were evaluated based on these indices using the Mann-Kendall trend test with removing all significant autocorrelation coefficients. The results from this study indicate that using Mann-Kendall test with removing all significant autocorrelation coefficients effects are essential in assessing trend in time series. Although, according to various studies available in the literature, SPI is known as more accurate than RAI in drought mitigation, but according the results of this study, can solely be used both RAI and SPI index for trend detection.
majid montaseri; sarvin zamanzad ghavidel
Abstract
Introduction: A total dissolved solid (TDS) is an important indicator for water quality assesment. Since the composition of mineral salts and discharge affects the TDS of water, it is important to understand the relationships of mineral salts composition with TDS.
Materials and Methods: In this study, ...
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Introduction: A total dissolved solid (TDS) is an important indicator for water quality assesment. Since the composition of mineral salts and discharge affects the TDS of water, it is important to understand the relationships of mineral salts composition with TDS.
Materials and Methods: In this study, methods of artificial neural networks with five different training algorithm,Levenberg-Marquardt (LM), Scaled Conjugate Gradient (SCG), Fletcher Conjugate Gradient (CGF), One Step Secant (OSS) and Gradient descent with adaptive learning rate backpropagation(GDA)algorithm and adaptive Neurofuzzy inference system based on Subtractive Clustering were used to model water quality properties of Zarrineh River Basin, to be developed in total dissolved solids prediction. ANN and ANFIS program code were written in MATLAB language. Here, the ANN with one hidden layer was used and the hidden nodes’ number was determined using trial and error. Different activation functions (logarithm sigmoid, tangent sigmoid and linear) were tried for the hidden and output nodes. Therefore, water quality data from seven hydrometer stationswere used during the statistical period of 18years (1993-2010).In this research, the study period was divided into two periods of dry and wet flow, and then in a preliminary statistical analysis, the main parameters affecting the estimation of the TDS are determined and isused for modeling. 75% of data are used for remaining and 25% of the data are used for evaluation of the model, randomly. In this paper, three statistical evaluation criteria, correlation coefficient (R), the root mean square error (RMSE) and mean absolute error (MAE) were used to assess models’ performances.
Results and Discussion: By applying correlation coefficients method between the parameters of water quality and discharge with total dissolved solid in two periods, wet and dry periods, the significant (at 95% level) variables entered into the model were Q, HCO3., Cl, So4, Ca, Na and Mg. The optimal ANN (LM) architecture used in this study consists of an input layer with seven inputs, one hidden and output layer with two and five neurons for dry and wet periods, respectively. Similar ANN(LM), ANFIS-SC model had the best performance. It is clear that the ANFIS with 0/72 and 0/58 radii value has the highest R and the lowest RMSE for dry and wet periods, respectively. Comparing the ANFIS-SC estimations with the measured data for the test stage demonstrates a high generalization capacity of the model, with relatively low error and high correlation. From the scatter plots it is obviously seen that the ANFIS-SC predictions are closer to the corresponding measured TDS than other models in two periods. As seen from the best straight line equations (assume the equation as y=ax) in the scatter plots that the coefficient for ANFIS-SC is closer to 1 than other models. In addition ANFIS-SC performancedwith the correlation coefficients in dry and wet periods, respectively 0.975 , 0.969 and with Root-mean-square errors, respectively 34.41 , 23.85 in order to predict dissolved solids (TDS) in the rivers of Zarrineh River Basin. The obtained results showed the efficiency of the applied models in simulating the nonlinear behavior of TDS variations in terms of performance indices. The results are also tested by using t test for verifying the robustness of the models at 99% significance level. Comparison results indicated that the poorest model in TDS simulation was ANN-GDAin dry and wet periods, especially in test period. The observed relationship between residuals and model computed TDS values shows complete independence and random distribution. It is further supported by the respective correlations for ANFIS-SC models (R2 = 0.0012 for dry period and R2 = 0.0214 for wet period) which are negligible small. Plots of the residuals versus model computed values can be more informative regarding model fitting to a data set. If the residuals appear to behave randomly it suggests that the model fits the data well. On the other hand, if non- random distribution is evident in the residuals, the model does not fit the data adequately. On the base of these results, we propose ANFIS-SC and ANN (LM) methods as effective tools for the computation of total dissolved solids in river water, respectively.
Conclusion: It can be concluded that the ANN with Levenberg-Marquardt training algorithm and ANFIS-SC models can be considered as promising tools for forecasting TDS values, based on water quality parameters. With attention to the aim of current research that is presenting the feasibility of artificial intelligence techniques for modeling TDS values, it is notable that the results presented in this paper are for research purpose and applying the abstained results for real-world needs some complicated steps and building artificial intelligences methods, based on complete data and parameters maybe affected the TDS values
sarvin ghavidel; sarvin zamanzad ghavidel
Abstract
Forecasts of streamflows are required for many activities associated with the planning and operation of components in a water resource system. This paper demonstrates the application of two different intelligent approaches including adaptive neuro-fuzzy (ANFIS) based on grid partition and Gene Expression ...
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Forecasts of streamflows are required for many activities associated with the planning and operation of components in a water resource system. This paper demonstrates the application of two different intelligent approaches including adaptive neuro-fuzzy (ANFIS) based on grid partition and Gene Expression Programming (GEP) for the prediction of monthly streamflows. In the first part of the study, ANFIS and GEP models were used in one-month ahead streamflow forecasting and the results were evaluated. Monthly run-off data of 21 years from two stations, the Safakhaneh Station on the Sarough-Chay Stream and the Senteh Station on the Kherkherh-Chay Stream in the Zarrineh-rud Basin of Iran were used in the study. The effect of periodicity on the model’s forecasting performance was also investigated. By application of periodicity coefficient in GEP model, determination coefficient in the case of the best input combination for Safakhaneh and Senteh increased 0.19 and 0.25, respectively. In the second part of the study, the performance of the ANFIS and GEP techniques was tested for streamflow estimation using data from the nearby river. The results indicated that the GEP and ANFIS models could be employed successfully in forecasting streamflow. In this case, for the best input combination, root mean square error (RMSE) for ANFIS and GEP obtained equal to 4.88 and 4.89 respectively. However, GEP is superior to ANFIS in giving explicit expressions for the problem.
J. Behmanesh; M. Montaseri
Abstract
Potential evapotranspiration is one of the most important and effective factors for optimizing agricultural water consumption and water resources management. One of methods for prediction of evapotranspiration is to use the time series models. In this research, application of different time series models, ...
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Potential evapotranspiration is one of the most important and effective factors for optimizing agricultural water consumption and water resources management. One of methods for prediction of evapotranspiration is to use the time series models. In this research, application of different time series models, such as AR and ARMA, in order to predicting monthly potential evapotranspiration in Urmia synoptic station were evaluated. In this process, monthly potential evapotranspiration since 1971 to 2010 was determined and the first 35 years and last 5 years were used for model calibration and validation respectively. After selecting the best model, the potential evapotranspiration were predicted for the next 5 years. The results showed that AR(11) time series model had the best results in comparing the other models and the trend of AR(11) time series model had least error. The values of R2 and RMSE in AR(11) model were 0.96 and 1.85 mm/month, respectively.