MohammadAmin Amini; Ghazaleh Torkan; Saeid Eslamian; Mohammad Javad Zareian; Ali Asghar Besalatpour
Abstract
Introduction: Understanding the concept of water balance is one of the most important prerequisites for sustainable management of water resources in the watersheds. Therefore, the components of water resources in a catchment system should be compared at different time periods, and also the effect of ...
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Introduction: Understanding the concept of water balance is one of the most important prerequisites for sustainable management of water resources in the watersheds. Therefore, the components of water resources in a catchment system should be compared at different time periods, and also the effect of each of them should be identified on varied hydraulic components of the hydrological systems. The SWAT model is an example of a physically based hydrologic model which can be used for large-scale simulating and monitoring of water cycle processes based on the characteristics of the catchment area and its climatic conditions. The main object of this study is the hydrologic simulation and water balance estimation for the period 2000-2009 in the Zayandeh-Rud River Basin.
Materials and Methods: The Zayandeh-Rud River Basin is located in the arid and semi-arid central region of Iran. This area is very variable in terms of rainfall. As well as the state of water resources and water consumption is very complicated in this catchment. In the present study, the soil and water assessment tool (SWAT) used to simulate water balance in the Zayandeh-Rud River Basin. The input required data included digital elevation model, land use map, soil texture map and meteorological information including daily rainfall data and minimum and maximum temperature data were introduced to the model and the model was implemented with these data. The sensitivity of the flow-effective parameters was determined using the p-value and t-state criteria by the SUFI2 algorithm in the SWAT-CUP program. The model was calibrated monthly and validated with the selected parameters in the sensitivity analysis using the Nash-Sutcliff criteria and the coefficient of determination by the application of the data of six stations including. Calibration of the model was conducted for 2000-2006 and validation of the model for the years 2007-2009.
Results and Discussion: The results of sensitivity analysis showed that considering the characteristics of the study area, the SWAT model is more sensitive to the 17 effective parameters on runoff. The selected parameters also confirm the results of previous research carried out in the region. The sensitive parameters selected in the sensitivity analysis step were used to calibrate the model. In the next step, the parameters of SWAT-CUP software were entered. After that, these parameters were repeated 1000 times with the SUFI2 algorithm, and the optimal value for each parameter was determined. The Nash-Sutcliff coefficient and the coefficient of determination in the six hydrometric stations are greater than 0.56 and 0.69 in calibration and verification periods respectively, which indicates that the model has a satisfactory ability to run in runoff simulation. The contribution of the components of the water balance including evapotranspiration, surface runoff, lateral flow, groundwater flow, and deep aquifer recharge was calculated from annual basin precipitation. The amount of extracted water from the hydrological components indicated that the largest share of the water balance was related to actual evapotranspiration, the range of variations in the type of precipitation in the study area ranged from 60.1% (2000) to 92.7 % (2007). After evapotranspiration, surface runoff with a change of 22.2% (2005) to 8.6% (2009) and groundwater flow with a change of 14.2% (2000) to 20.5% (the year 2007) had relatively high fluctuations and a large share in the basin balance. These results indicate that the lateral flow with a range of 3.1 to 1.9% had no significant change in these years. Also, the deep aquifer recharge with the range of 1.2 to1.5% was the lowest in 2003 and 2009, respectively.
Conclusion: The results showed that the calibrated model for the Zayandeh-Rud River Basin had a desirable performance for both calibration and validation periods. Therefore, the SWAT model has acceptable performance for simulating the water balance of the area. In addition, the results of this study showed that 65.98% of the total annual precipitation in the basin is in form of evapotranspiration, which compares to the other water balance components has the highest part. As well as surface runoff with 15%, groundwater flow with 13.7%, lateral flow with 1.5%, and deep aquifer recharge with 0.8% have other parts of the water balance components in Zayandeh-Rud River Basin. The results also indicate that the highest water losses in the soil and groundwater resources of the basin are due to evapotranspiration. Therefore, serious measures to prevent the loss of water through evapotranspiration in the region to be necessary. The results of this research can be used to predict the effects of climate change and the applicable management practices in the region, which are presented in scenarios to the model.
F. Khadempour; B. Bakhtiari; S. Golestani
Abstract
Introduction: In drainage and irrigation network capacity design and determination, reference evapotranspiration (ETo) plays significant role. Methods applied for estimated reference evapotranspiration classified in two direct and computational methods. Amongst computational methods it might point to ...
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Introduction: In drainage and irrigation network capacity design and determination, reference evapotranspiration (ETo) plays significant role. Methods applied for estimated reference evapotranspiration classified in two direct and computational methods. Amongst computational methods it might point to Penman-Monteith method. This method requires radiation, temperature, humidity and wind speed data with high reliability rate in vast ranges of climates and areas represent precise outcome from reference plant Evapotranspiration.
Materials and Methods: Study stations in De Martonne classification system are divided into 6 climates such as Hyper-arid, Arid, Semi-arid, Mediterranean, Humid and Very humid (a) climates. Study stations statistical span during 19 years (1996-2015) were selected and temperature, relative humidity, sunshine hours, and wind speed in 2 meter height daily data were used. Figure 1 showed studied stations position all over the country. In this study, in order to obtain daily ETo, Penman-Monteith standard method represented by FAO-56 was used. In local sensitivity analysis, factors local influences on model output were shown. Such an analysis usually carried out through output functions minor deviants computation due to input variables. In this analysis, usually it was used one-factor- at-a- time method (OAT), so that, one variable factor and other input factors kept constant.
Figure 1. The geographical location of weather stations
The FAO-56 PM model for estimating ETo is as follows (3).
(1)
where ETo is reference crop evapotranspiration (mm day−1), Δ is the slope of vapor pressure versus temperature curve at temperature Tmean (kPa°C−1), γ is the psychometric constant (kPa °C−1), u2 is the wind speed at a 2 m height (m s−1), Rn is the net radiation at crop surface (MJ m−2 d−1), G is the soil heat flux density (MJ m−2 d−1), T is the mean daily air temperature at 2 m height (°C), and (es-ea) is the saturation vapor pressure deficit (kPa).
Results and Discussion: Weather parameters in stations showed that mean temperature sensitivity coefficient ( ) in all study stations varied between 0.21 to 0.78 so that the maximum temperature sensitivity coefficient related to Bushehr station in arid climate (in April, May, June, July, October and November) and minimum temperature sensitivity coefficient related to Shahrekordstation in semi-arid climate (in January, March, April and November). Maximum and minimum net radiation sensitivity coefficient value ( ) related to Rasht and Zahedanstations respectively. Also, maximum and minimum wind speed sensitivity coefficient value ( ) related to Zahedan and Ardebilstations are 0.54 and 0.07 respectively. Yazd station in Hyper-arid climate showed minimum relative humidity sensitivity coefficient value ( ) about 0.20 and Rasht station in very-humid (a) showed the maximum values 0.45. So the northern coastal areas are more sensitive to and SRH. The highest value is in northern coastal areas and lowest in southern coastal and southwest areas of the country. Some other studies showed that in many climates evapotranspiration was more sensitive to Rn (6, 14 and 17).In current study, also, showed the highest sensitivity in Very-humid climate (a) includes Rasht station in February, March, April, October and November. For example, = 0.82 means that 100% increase in Rn parameter result in 82% increase in ETo.
Conclusion: Sensitivity analysis experiment on FAO Penman-Monteith standard method is one of the most efficient methods to understand various climate parameters influence on reference evapotranspiration (ETo). In this study, results showed that computed ETo in all climates showed highest sensitivity to Rn and temperature respectively. Temperature sensitivity coefficient showed the highest value at April. May, June, July, October and November and Rn showed its highest value at March, April, October and November. While, minimum in all of months but May and July and maximum value showed in January, July, August and September by 0.07 and 0.54 respectively. So, in most months of the spring and the fall was larger and smaller during the winter months. Sensitivity coefficient related to mean temperature is higher during summer season and lower during winter season. Results of this study may be useful for assessing the response of the standardized FAO Penman-Monteith model in different climatic conditions. The results can also be used to predict changes in ETo values with respect to climatic variable changes obtained from climate change models.
aboalhasan fathabadi; hamed rouhani
Abstract
Introduction: In order to implement watershed practices to decrease soil erosion effects it needs to estimate output sediment of watershed. Sediment rating curve is used as the most conventional tool to estimate sediment. Regarding to sampling errors and short data, there are some uncertainties in estimating ...
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Introduction: In order to implement watershed practices to decrease soil erosion effects it needs to estimate output sediment of watershed. Sediment rating curve is used as the most conventional tool to estimate sediment. Regarding to sampling errors and short data, there are some uncertainties in estimating sediment using sediment curve. In this research, bootstrap and the Generalized Likelihood Uncertainty Estimation (GLUE) resampling techniques were used to calculate suspended sediment loads by using sediment rating curves.
Materials and Methods: The total drainage area of the Sefidrood watershed is about 560000 km2. In this study uncertainty in suspended sediment rating curves was estimated in four stations including Motorkhane, Miyane Tonel Shomare 7, Stor and Glinak constructed on Ayghdamosh, Ghrangho, GHezelOzan and Shahrod rivers, respectively. Data were randomly divided into a training data set (80 percent) and a test set (20 percent) by Latin hypercube random sampling.Different suspended sediment rating curves equations were fitted to log-transformed values of sediment concentration and discharge and the best fit models were selected based on the lowest root mean square error (RMSE) and the highest correlation of coefficient (R2). In the GLUE methodology, different parameter sets were sampled randomly from priori probability distribution. For each station using sampled parameter sets and selected suspended sediment rating curves equation suspended sediment concentration values were estimated several times (100000 to 400000 times). With respect to likelihood function and certain subjective threshold, parameter sets were divided into behavioral and non-behavioral parameter sets. Finally using behavioral parameter sets the 95% confidence intervals for suspended sediment concentration due to parameter uncertainty were estimated. In bootstrap methodology observed suspended sediment and discharge vectors were resampled with replacement B (set to 3000) times. Sediment rating curves equation was fitted to each sampled suspended sediment and discharge data sets. Using these sediment rating curve and their residual suspended sediment concentration were calculate for test data. Finally using the 2.5 and 97.5 percentile of the B bootstrap realizations, 95% bootstrap prediction intervals were predicted.
Results and Discussion: Results showed that Motorkhane and MiyaneTonelShomare 7 stations were best fitted by a sigmoid function and Stor and Glinak stations were best fitted by second order polynomial and liner function, respectively The first 50 of the B bootstrapped curves were plotted for all stations.with respect to these plots implied that bootstrapped curves more scattered whereas observed data were less. The suspended sediment curve parameters estimated more accurately where, the suspended sediments were sampled more, as a result of reduced uncertainty in estimated suspended sediment concentration due to parameter uncertainty. In addition to sampling density bootstrapped curves, uncertainty depends on the curve shape. For GLUE methodology to assess the impact of threshold values on the uncertainty results, threshold values systematically changed from 0.1 to 0.45. Study results showed that 95% confidence intervals are sensitive to the selected threshold values and higher threshold values will result in an increasing 95% confidence interval. However, the highest 95% confidence intervals obtained by GLUE method (when threshold value was set to 0.1) was little than those values obtained by Bootstrap.
Conclusions: The uncertainty of sediment rating curves was addressed in this study by considering two different procedures based on the GLUE and bootstrap methods for four stations in Sefidrod watershed.Results showed that nonlinear equation fitted log-transformed values of sediment concentration and discharge better than linear equation. Uncertainty result using GLUE depend on chosen threshold values. As threshold values increased, 95% confidence intervals decreased. Uncertainty results showed that 95% confidence intervals estimated by bootstrap were higher than the biggest 95% confidence intervals (when threshold value set to 0.1) estimated by GLUE method. Overall, in all stations, 95% confidence intervals arising from suspended sediment curve shapes (e.g, linear, second order polynomial and sigmoid function), data sampling density and uncertainty estimation methods (here were GLUE and Bootstrap).
maysam majidi; a. Alizade; m. vazifedoust; a. faridhosseini
Abstract
Introduction: Water when harvested is commonly stored in dams, but approximately up to half of it may be lost due to evaporation leading to a huge waste of our resources. Estimating evaporation from lakes and reservoirs is not a simple task as there are a number of factors that can affect the evaporation ...
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Introduction: Water when harvested is commonly stored in dams, but approximately up to half of it may be lost due to evaporation leading to a huge waste of our resources. Estimating evaporation from lakes and reservoirs is not a simple task as there are a number of factors that can affect the evaporation rate, notably the climate and physiography of the water body and its surroundings. Several methods are currently used to predict evaporation from meteorological data in open water reservoirs. Based on the accuracy and simplicity of the application, each of these methods has advantages and disadvantages. Although evaporation pan method is well known to have significant uncertainties both in magnitude and timing, it is extensively used in Iran because of its simplicity. Evaporation pan provides a measurement of the combined effect of temperature, humidity, wind speed and solar radiation on the evaporation. However, they may not be adequate for the reservoir operations/development and water accounting strategies for managing drinking water in arid and semi-arid conditions which require accurate evaporation estimates. However, there has not been a consensus on which methods were better to employ due to the lack of important long-term measured data such as temperature profile, radiation and heat fluxes in most lakes and reservoirs in Iran. Consequently, we initiated this research to find the best cost−effective evaporation method with possibly fewer data requirements in our study area, i.e. the Doosti dam reservoir which is located in a semi-arid region of Iran.
Materials and Methods: Our study site was the Doosti dam reservoir located between Iran and Turkmenistan borders, which was constructed by the Ministry of Water and Land Reclamation of the Republic of Turkmenistan and the Khorasan Razavi Regional Water Board of the Islamic Republic of Iran. Meteorological data including maximum and minimum air temperature and evaporation from class A pan were acquired from the Doosti Dam weather station. Relative humidity, wind speed, atmospheric pressure and precipitation were acquired from the Pol−Khatoon weather station. Dew point temperature and sunshine data were collected from the Sarakhs weather station. Lake area was estimated from hypsometric curve in relation to lake level data. Temperature measurements were often performed in 16−day periods or biweekly from September 2011 to September 2012. Temperature profile of the lake (required for lake evaporation estimation) was measured at different points of the reservoir using a portable multi−meter. The eighteen existing methods were compared and ranked based on Bowen ratio energy balance method (BREB).
Results and Discussion: The estimated annual evaporation values by all of the applied methods in this study, ranged from 21 to 113mcm (million cubic meters). BREB annual evaporation obtained value was equal to 69.86mcm and evaporation rate averaged 5.47mm d-1 during the study period. According to the results, there is a relatively large difference between the obtained evaporation values from the adopted methods. The sensitivity analysis of evaporation methods for some input parameters indicated that the Hamon method (Eq. 16) was the most sensitive to the input parameters followed by the Brutsaert−Stricker and BREB, and radiation−temperature methods (Makkink, Jensen−Haise and Stephen−Stewart) had the least sensitivity to input data. Besides, the air temperature, solar radiation (sunshine data), water surface temperature and wind speed data had the most effect on lake evaporation estimations, respectively. Finally, all evaporation estimation methods in this study have been ranked based on RMSD values. On a daily basis, the Jensen−Haise and the Makkink (solar radiation, temperature group), Penman (Combination group) and Hamon (temperature, day length group) methods had a relatively reasonable performance. As the results on a monthly scale, the Jensen−Haise and Makkink produced the most accurate evaporation estimations even by the limited measurements of the input data.
Conclusion: This study was carried out with the objective of estimating evaporation from the Doosti dam reservoir, and comparison and evaluation of conventional method to find the most accurate method(s) for limited data conditions. These examinations recognized the Jensen−Haise, Makkink, Hamon (Eq. 17), Penman and deBruin methods as the most consistent methods with the monthly rate of BREB evaporation estimates. The results showed that radiation−temperature methods (Jensen−Haise and Makkink) have appropriate accuracy especially on a monthly basis. Also deBruin, Penman (combination group), Hamon and Papadakis (temperature group) methods produced relatively accurate results. The results revealed that it is necessary to calibrate and adjust some evaporation estimation methods for the Doosti dam reservoir. According to the required input data, sensitivity and accuracy of these methods, it can be concluded that Jensen−Haise and Makkink were the most appropriate methods for estimating the lake evaporation in this region especially when measured data were not available.
M. Mohammadi; B. Ghahraman; K. Davary; H. Ansari; A. Shahidi
Abstract
Introduction: FAO AquaCrop model (Raes et al., 2009a; Steduto et al., 2009) is a user-friendly and practitioner oriented type of model, because it maintains an optimal balance between accuracy, robustness, and simplicity; and it requires a relatively small number of model input parameters. The FAO AquaCrop ...
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Introduction: FAO AquaCrop model (Raes et al., 2009a; Steduto et al., 2009) is a user-friendly and practitioner oriented type of model, because it maintains an optimal balance between accuracy, robustness, and simplicity; and it requires a relatively small number of model input parameters. The FAO AquaCrop model predicts crop productivity, water requirement, and water use efficiency under water-limiting and saline water conditions. This model has been tested and validated for different crops such as maize, sunflower and wheat (T. aestivum L.) under diverse environments. In most of arid and semi-arid regions water shortage is associated with reduction in water quality (i.e. increasing salinity). Plants in these regions in terms of water quality and quantity may be affected by simultaneous salinity and water stress. Therefore, in this study, the AquaCrop model was evaluated under simultaneous salinity and water stress. In this study, AquaCrop Model (v4.0) was used. This version was developed in 2012 to quantify the effects of salinity. Therefore, the objectives of this study were: i) evaluation of AquaCrop model (v4.0) to simulate wheat yield and water use efficiency under simultaneous salinity and water stress conditions in an arid region of Birjand, Iran and ii) Using different treatments for nested calibration and validation of AquaCrop model.
Materials and Methods: This study was carried out as split plot design (factorial form) in Birjand, east of Iran, in order to evaluate the AquaCrop model.Treatments consisted of three levels of irrigation water salinity (S1, S2, S3 corresponding to 1.4, 4.5, 9.6 dS m-1) as main plot, two wheat varieties (Ghods and Roshan), and four levels of irrigation water amount (I1, I2, I3, I4 corresponding to 125, 100, 75, 50% water requirement) as sub plot. First, AquaCrop model was run with the corresponding data of S1 treatments (for all I1, I2, I3, and I4) and the results (wheat grain yield, average of soil water content, and ECe) were considered as the “basic outputs”. After that and in the next runs of the model, in each step, one of the inputs was changed while the other inputs were kept constant. The interval of variation of the inputs was chosen from -25 to +25% of its median value. After changing the values of input parameters, the model outputs were compared with the “basic outputs” using the sensitivity coefficient (Sc) of McCuen, (1973). Since there are four irrigation treatments for each salinity treatment, the model was calibrated using two irrigation treatments for each salinity treatment and validated using the other two irrigation treatments. In fact, six different cases of calibration and validation for each salinity treatment were [(I3 and I4), (I2 and I4), (I1 and I4), (I2 and I3), (I1 and I3), and (I1 and I2) for calibration and (I1 and I2), (I1 and I3), (I2 and I3), (I1 and I4), (I2 and I4), and (I3 and I4) for validation, respectively]. The model was calibrated by changing the coefficients of water stress (i.e. stomata conductance threshold (p-upper) stomata stress coefficient curve shape, senescence stress coefficient (p-upper), and senescence stress coefficient curve shape) for six different cases. Therefore, the average relative error of the measured and simulated grain yield was minimized for each case of calibration. After calibrating the model for each salinity treatment, the model was simultaneously calibrated using six different cases for three salinity treatments as a whole.
Results and Discussion: Results showed that the sensitivity of the model to crop coefficient for transpiration (KcTr), normalized water productivity (WP*), reference harvest index (HIo), θFC, θsat, and maximum temperature was moderate. The average value of NRMSE, CRM, d, and R2 for soil water content were 11.76, 0.055, 0.79, and 0.61, respectively and for soil salinity were 24.4, 0.195, 0.72, and 0.57, respectively. The model accuracy for simulation of soil water content was more than for simulation of soil salinity. In general, the model accuracy for simulation yield and WP was better than simulation of biomass. The d (index of agreement) values were very close to one for both varieties, which means that simulated reduction in grain yield and biomass was similar to those of measured ones. In most cases the R2 values were about one, confirming a good correlation between simulated and measured values. The NRMSE values in most cases were lower than 10% which seems to be good. The CRM values were close to zero (under- and over-estimation were negligible). Based on higher WP under deficit irrigation treatments (e.g. I3) compared to full irrigation treatments (e.g. I1 and I2), it seems logical to adopt I3 treatment, especially in Birjand as a water-short region, assigning the remaining 25% to another piece of land. By such strategy, WP would be optimized at the regional scale.
Conclusion: The AquaCrop was separately and simultaneously nested calibrated and validated for all salinity treatments. The model accuracy under simultaneous case was slightly lower than that for separate case. According to the results, if the model is well calibrated for minimum and maximum irrigation treatments (full irrigation and maximum deficit irrigation), it could simulate grain yield for any other irrigation treatment in between these two limits. Adopting this approach may reduce the cost of field studies for calibrating the model, since only two irrigation treatments should be conducted in the field. AquaCrop model can be a valuable tool for modelling winter wheat grain yield, WP and biomass. The simplicity of AquaCrop, as it is less data dependent, made it to be user-friendly. Nevertheless, the performance of the model has to be evaluated, validated and fine-tuned under a wider range of conditions and crops.
Keywords: Biomass, Plant modeling, Sensitivity analysis
M. Makari; B. Ghahraman; S.H. Sanaeinejad
Abstract
The objective of this study is to analyze the sensitivity of ETo for five models including FAO-Penman-Monteith, modified Blaney-Criddle, Hargreaves, Hargreaves-Samani and Priestley –Taylor. Daily meteorological data of Bojnourd synoptic station including air temperature, relative humidity, actual duration ...
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The objective of this study is to analyze the sensitivity of ETo for five models including FAO-Penman-Monteith, modified Blaney-Criddle, Hargreaves, Hargreaves-Samani and Priestley –Taylor. Daily meteorological data of Bojnourd synoptic station including air temperature, relative humidity, actual duration sunshine and wind velocity were used for sensitivity analysis of five models. In order to produce random data at a specific range, Monte-Carlo simulation was performed. Annual and seasonal were calculated to indicate the sensitivity of ETo in simultaneous variations of meteorological variables in each model.The results obtained in this study showed that the sensitivity of in simultaneous variations of meteorological variables is higher in summer. In all models, the most sensitivity was seen in summer and spring and the least sensitivity was occurred in autumn and winter. Among the studied models, FAO-PM and BC models had the most annual sensitivity and PT model had the least annual sensitivity. All of the models had fairly high correlation coefficient with FAO-PM model but the quantity of and was different in each model. BC model had the most and the least and was seen in and PT. According to the findings in this study, it can be concluded that SH model is fairly suitable for estimation of in synoptic station.
shamsollah Ayoubi; Mohammad Reza Mosaddeghi
Abstract
Soil surface shear strength is an important parameter for prediction of soil erosion, but its direct measurement is difficult, time-consuming and costly in the watershed scale. This study was done to predict soil surface shear strength using artificial neural networks (ANNs) and multiple linear regression ...
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Soil surface shear strength is an important parameter for prediction of soil erosion, but its direct measurement is difficult, time-consuming and costly in the watershed scale. This study was done to predict soil surface shear strength using artificial neural networks (ANNs) and multiple linear regression (MLR) and to rank the most important soil and environmental attributes affecting the shear strength. A direct shear box was designed and constructed to measure in situ soil surface shear strength. The device can determine two soil shear strength parameters i.e. cohesion (c) and angle of internal friction (φ). The study area (3500 km2) was located in Semirom region, Isfahan province. Soil surface shear strength was determined using the shear box at 100 locations. Soil samples were also collected from 0-5 cm layer of the same 100 locations at which the surface shear strength was measured using the shear box. Particle size distribution, fine clay content, organic matter content (OM), carbonate content, bulk density and gravel content were determined on the collected soil samples. Normalized difference vegetation index (NDVI), the type of land use and geology were also determined. The MLR and ANNs were used to model/predict soil surface shear strength (c and φ). In order to compare the modeling methods, coefficient of determination and root mean square errors were used as efficacy indices. The results showed that ANN models were more feasible in predicting soil shear strength parameters than MLR models due to capability of ANN models in deriving nonlinear and complex relations between the parameters. Results of sensitivity analysis for ANN models indicated that NDVI, bulk density and fine clay content are more effective parameters in predicting c in the studied region. Also it was found that sand content, bulk density and NDVI were more effective parameters and OM/clay ratio and organic matter content were less effective parameters in predicting φ in the region.
habib beigi
Abstract
Boroujen–Fradonbeh plain is one of the nine main agricultural hubs of Charmahal Provine. The aim of this study was to define and map a deficiency index of soil micronutrients and the effect of wastewater application on it. For this, 200 surface soil (0-30 cm) samples were randomly collected and plant ...
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Boroujen–Fradonbeh plain is one of the nine main agricultural hubs of Charmahal Provine. The aim of this study was to define and map a deficiency index of soil micronutrients and the effect of wastewater application on it. For this, 200 surface soil (0-30 cm) samples were randomly collected and plant available concentrations of copper, zinc, iron, and manganese were determined. After variography and determining the most suitable spatial estimation method, maps of each micronutrient was drawn, normalized, and ranked. An integrated deficiency map was then constructed using the weights from rank maps. According to the maps of copper, zinc and iron, the available concentrations increased from west to east of the plain. This increase was attributed to the wastewater irrigation. The mean value of the integrated map, namely 85.5, indicated the seroius soil deficiency of micronutrients in this plain where 34% of the area was showing severe deficiency. Wastewater application has increased the overall availability of micronutrients by 4%. Sensivity analysis indicated that the map was most sensitive to zinc. Therefore, zinc concentration must be monitored with more precision and frequency across the plain.
A. Moghaddam; A. Alizadeh; Alinaghi Ziaei; A. Farid Hosseini; D. Fallah Heravi
Abstract
Genetic Algorithm as a one of the main evolutionary algorithms has had a most successful role in the water distribution network optimization.This algorithmhas been undergoing many reforms and improved versions are published. A type of genetic algorithms is Fast Messy Genetic Algorithm (FMGA), that has ...
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Genetic Algorithm as a one of the main evolutionary algorithms has had a most successful role in the water distribution network optimization.This algorithmhas been undergoing many reforms and improved versions are published. A type of genetic algorithms is Fast Messy Genetic Algorithm (FMGA), that has the ability to increase the convergence rate in solving optimization problems with reducing the length of chromosomes and removing the inefficient genes, meanwhile studying the chromosomes which are not equal in terms of gene strings.In this paper, for evaluation of the FMGA performance in solving water distribution network optimization problems, after the sensitivity analysis and determining the best values of these parameters, two benchmark networks and a real network are analyzed, which are named Two-loop network, the Hanoi network and Jangal City network, respectively, and the results were compared with previous researches. Least-cost in two loop network was estimated after 2880 number of function evaluations that had significant improvements compared to the results of previous researches. In Hanoi network, the minimum cost obtained equal to 6.045×106 $ that is less than other researchers results are issued so far. After proving the efficiency of algorithm, its performance was shown in design of real Jangal city network according to increasing network size and design constraints.
H. Kashi; H. Ghorbani; S. Emamgholizadeh; S.A.A. Hashemi
Abstract
With respect to the problem of direct measurement of soil parameters in recent year using indirect method such as artificial neural networks has been considered. In the present study, 200 soil samples were collected from Ghoshe location in Semnan province. Half of samples were collected from disturbed ...
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With respect to the problem of direct measurement of soil parameters in recent year using indirect method such as artificial neural networks has been considered. In the present study, 200 soil samples were collected from Ghoshe location in Semnan province. Half of samples were collected from disturbed agricultural lands and the other half were collected from undisturbed nearby lands. Some soil chemical as well as physical properties such as electrical conductivity (EC), soil texture, lime percentage, sodium adsorption ration (SAR) and bulk density were considered as easy and fast obtainable features and soil cation exchange capacity as difficult and time consuming feature. The collected data randomly divided in two categories of training (70%) and testing (30%) and they used for train and test of two artificial neural networks, multi-layer perception using back-propagation algorithm (MLP/BP) and Radial basis functions (RBF) and nonlinear regression model. Results of this research show high efficiency of artificial neural network compared with nonlinear regression and also MLP network was better than RBF network. Sensitivity analysis was also performed for all parameters to find out the relationship between soil mentioned parameters and soil cation exchange capacity for both disturbed and undisturbed soils. At last, the correlation between soil parameters and soil cation exchange capacity was determined and most important parameters which could influence the soil cation exchange capacity were described.
