فهرست مطالب maryam bayatvarkeshi

The main purpose of this study is to evaluate the effect of type of water resource in simulating water quality parameters in Savel plain by the intelligent models. In this investigation, the performance of ANN and CANFIS models in simulation of TDS, SAR, EC, and TH were compared with each other. For this aim, considering the correlation of the parameters, the Ph, Ca, Mg, Cl, and SO4 from 1966 to 2016 were selected as the input variables. The results indicated that the minimum error by the ANN in groundwater and surface water resources were related to the TH and the EC with 1.29 and 5.3 % respectively. The maximum error in both resources corresponded to the SAR. The same results were obtained by the CANFIS. The consequences of the T-Test showed that there was a significant difference between the ANN and the CANFIS in estimation of the SAR in the groundwater. So that the value of NRMSE decreased from 32.9% in the ANN to 22.82% in the CANFIS.  Concerning the other parameters, there was not any significant distinction. In case of the surface water, the CANFIS led to increase of proficiency of simulation of the SAR and the TDS. So thar, the value of the NRMSE declined from 60.35 and 29.27% in the ANN to 28.72 and 16.85 % in the CANFIS. In general, the impact of the type of the water resource in the accuracy of intelligent models in simulation of water quality parameters was approved.

Due to increasing gap between water supply and demand, paying close attention to the principles of optimal water allocation is inevitable. In the present investigation, allocations from Kalan Malayer Reservoir to demands identified by the Iranian Ministry of Energy were evaluated using MODSIM8.1. These demands are environmental and sustainability requirements, City of Malayer drinking water, agricultural requirement of the downstream lands, irrigation of Sahand Malayer farms and industrial centers. The analysis temporal scale was a 12-month period with monthly time steps during year 2016-2017. The purpose of this study was to evaluate the behavior of the dam reservoir when the inflow is reduced. Four scenarios including 5, 10, 15 and 20 percent river reduction in flow to the reservoir were investigated. The results indicated that with decrease in the river inflow, the stored water in the dam reservoir would decreased and there would be shortage to meet all demands. The deficiencies volume in supplying water to demands from the first scenario to the fourth scenario was 0.373, 0.748, 1.124 and 1.508 MCM, respectively. In other words, the relationship between the decrease in stored water volume in the dam reservoir and river inflow reduction was linear. Therefore, in order to meet the demands in all four scenarios, the allocation to the demand points should be reduced according to the priorities of the demands. The other solution is to reduce the target storage volume equal to the total deficiency volume in that month.

A reliable model for any wastewater treatment plant (WWTP) is essential to predict its performance and form a basis for controlling the operation of the process. This would minimize the operation costs and assess the stability of environmental balance. This study applied artificial neural network-genetic algorithm (ANN-GA) and co-active neuro-fuzzy logic inference system (CANFIS) in comparison with ANN for predicting the performance of WWTP. The result indicated that the GA produces more accurate results than fuzzy logic technique. It was found that GA components increased the ANN ability in predicting WWTP performance. The normalized root mean square error (NRMSE) for ANN-GA in predicting chemical oxygen demand (COD), total suspended solids (TSS) and biochemical oxygen demand (BOD) were 0.15, 0.19 and 0.15, respectively. The corresponding correlation coefficients were 0.891, 0.930 and 0.890, respectively. Comparing these results with other studies showed that despite the slightly lower performance of the current model, its requirement for a lower number of input parameters can save the extra cost of sampling.

Due to excessive withdrawal of groundwater, it's modeling in warm and arid regions are of very importance. Prediction of groundwater is important from different aspects of water resource management. In current years, the application of intelligent models in simulation has good results because of simplicity and high accuracy. Based on the previous studies, tin more investigations were used weather and hydrogeology parameters as input in intelligent models. While the effect of the ENSO phenomenon on climate and hydrology and hydrogeology was evaluated. The purpose of present study was an evaluation of intelligent models including artificial neural network (ANN) and co-active neuro-fuzzy inference system (CANFIS), wavelet transform combined with ANN (WANN) and WCANFIS models in forecasting groundwater level base on ENSO indexes.

Nitrate is among major anions in drinking water. Therefore, it is crucial to investigate its concentration in water resources. Modeling is a management strategy to predict the behavior of nitrate in water resources.

The current study aimed to predict nitrate concentration in the aquifer of Qazvin plain, using Groundwater Modeling System (GMS).

 The GMS7.1 software was used to prepare the groundwater flow model. MODFLOW pattern was used to investigate the three-dimensional flow. MT3D pattern was used to assess the changes nitrate concentration in the aquifer. Additionally, several scenarios were designed to evaluate the process of nitrate changes in the aquifer by altering in the aquifer feed and nitrate input rates.

 There was no anomaly concentration in the aquifer. The scenarios of rainfall reduction suggested that nitrate feed was ineffective in changing nitrate concentration in the aquifer. Moreover, with entry 100, 200, and 300 mg/L of nitrate into the aquifer, the average of nitrate concentration after the end of the two-year periods would increase to 31, 55, and 100mg/L, respectively.

The obtained results indicated that in a wide area of the saturated aquifer nitrate concentration was below than national standards. Furthermore, high nitrate concentrations were due to the penetrating of the sewage into the aquifer. The outputs of the GMS model suggestd that these conditions could lead to more contamination in central parts of the aquifer.

Planning and deciding about consuming water in future, and optimized management of water in line with sustainable agriculture development, need the information the climate change. In this study, the effect of climate change on temperature, solar radiation and precipitation, were evaluated. Daily weather parameters of 30 synoptic stations during 1991 to 2010 were used. HADCM3 in downscaling model of LARS-WG under r scenarios A1B, A2 and B1 for three 34-year periods from 2011 to 2045, 2046 to 2079 and 2080 to 2113 were applied to produce future data. The results of evaluation of model in basic period showed that the maximum accuracy of model belongs to the maximum temperature and the minimum accuracy is related to precipitation. In fact, NRMSE and r values of Tmax were 0.005 and 0.999, while they were 0.079 and 0.996 for the precipitation respectively. The prediction of weather parameters in future years indicated that the temperature and the solar radiation values will increase. However, the similar trend was not observed in precipitation. The scenario A2 was considered as the most crisis scenario, so that the increasing 2.13% solar radiation and 24.56 % temperature and decreasing 11.59 % precipitation until 2113 are predicted. In general, with continuing the present condition and prediction of future climate change, the optimized management in agriculture and water resources is necessary.

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.

Snow is an important hydrological phenomenon and snow water equivalent is suitable water resource in many parts of the world. Snow and snow water equivalent have a significant contribution in streamflow and groundwater recharge. For this reason, it is important modeling of snow accumulation and melting. So, estimation of snow spatial distribution in different time scales is one of key stages in water resources studies.
Due to the successful application of geostatistical methods in different sciences, the purpose of this study is mapping of snow characteristics. In this study, the spatial analysis of snow water equivalent, snow depth and snow density, which is one of major components of the water balance, is evaluated in watershed of the country north-west.
In this study, using geostatistical methods, spatial distribution of snow height, snow density and snow water equivalent were estimated. So, by measurement data of three Azabbayjan- Sharghi, Azarbayjan- Gharbi and Ardebi provinces during four years (2008-2012) in north-west, capability of Kriging, radial basis function and inverse distance weight method were evaluated. The Figure 1 shows location of study area and used data.
For estimation of snow characterizes in non-measurement estimated points was used evaluation, longitude and latitude parameters. The results comparison of each geostatistical methods was done by the Normal Root Mean Square Error (NRMSE) criterion.
(1) where Xi , Yi: ith estimated snow data , n: number examples. The drawing of zoning maps was done by ArcGIS software.
The before zoning, correlation coefficient value of snow density, snow height and snow water equivalent as depended with geographic characterizes was obtain in SPSS software (Table 1).
Table 1. the correlation coefficient matrix of used variables
Longitude Latitude Elevation Snow density Snow water equivalent Snow height
Longitude 1
Latitude -0.456** 1
Elevation 0.276* 0.105 1
Snow density 0.167 -0.053 0.221 1
Snow water equivalent 0.270* -0.107 0.489** 0.410** 1
Snow height 0.218 -0.103 0.500** 0.035 0.893** 1
In addition to Table 2, elevation and longitude with correlation coefficient of 0.489 and 0.270, respectively, have the most effect on snow water equivalent. the positive sign indicates straight relative between elevation and longitude with snow water equivalent.
As a general result, each three snow characterizes have positive relative with elevation. it is because of elevation is an important topography factor and increased height leads decreased air temperature and enhancement snow.
The results indicated that in 100% cases, the ordinary kriging method with Gaussian semi variogram had the best results than all methods. the results of inverse distance weight method showed that this method had the least accurate in zoning. Even, power increased dont lead than accurate enhancement. Nikbakht and Delbari (2013) applied different interpolation methods for estimated ground water table in Zahedan plain. in this study reported preference of kriging method with Gaussian semi variogram. McKenna (2000) reported that kriging geostatistical method is suitable, also can be used low number data. The results indicated that mean accurate of kriging method with Guassian semi variogram for snow density, snow water equivalent during four years on base Normal Root Mean Square Error (NRMSE) were 6.76, 3.18 and 10.57, respectively. the results of mapping showed that the most zones of snow characterizes were located in two middle classis and minimum and maximum zones were as small spots.
The purpose of this research was to develop interpolation methods to assess the estimation snow components in the non-measurement points. In addition to equipment and preparation problems of snow stations, it is necessary to use modern methods to inform from the snow spatial distribution. The results of this study showed that in the study area and in four years time, ordinary kriging gave better results than other methods. the different interpolation methods had different accurate and can not extend results of an area easily to other areas. But the advantage of this research was in using four years data to an area with 100,861 km2 zone and for three snow important features. Other features of the study was in using longitude, latitude and elevation in non-measured points. Since the used independent variables located in readily available variables categories (access quick to the data at a lower cost and higher accuracy), so we can expect good results with high accurate.

Nitrogen is needed in plants to form chlorophyll as well as to increase crop protein and plant yield. Application of too much nitrogen-containing fertilizers, however, is not economical. It has the additional disadvantage that it ultimately ends up in groundwater resources. Potato production in light soils usually requires a high consumption of nitrogen and frequent irrigation. At the beginning of the growing season, potatoes need a lot of nitrogen. High consumption of it at the end of the growing season, however, will cause increased foliage instead of larger tubers. Studies with high levels of nitrogen input aimed at comparing fertilizer regimes have also shown differences in nitrogen use efficiency (NUE). In recent years, the use of nanotechnology has been considered. With respect to the food recommended in most cultures, and limited research on the efficacy of nanofertilizers on crops, the present study is meant to evaluate the effectiveness of two types of nitrogen nanofertilizers, sulfur-coated urea and urea fertilizer, on potato crops.
The effect of nitrogen fertilizers on nitrogen efficiency in potato cultivation were investigated by performing a factorial experiment based on a completely randomized design with four fertilizer treatments. These included applications of Nano-Nitrogen Chelate (NNC), Sulphur Coated Nano-Nitrogen Chelate (SNNC), Sulphur Coated Urea (SCU) and Urea (U) in the greenhouse over two years. Each treatment, including 46 kg-N/ha, 92 kg-N/ha, and 138 kg-N/ha, with three replications were compared. The study parameters included nitrogen efficiency indices, leaf nitrogen percentage, yield and tuber nitrate. Also calculated for each treatment were the N-efficiency parameters of Agronomic-Nitrogen Use Efficiency (A-NUE), Economic-Nitrogen Use Efficiency (E-NUE), Nitrogen Uptake Percentage (NUP), Agronomic-Physiologic Nitrogen Use Efficiency (A-PNUE) and Economic-Physiologic Nitrogen Use Efficiency (E-PNUE)
The results showed that the maximum nitrate concentration of 240 mg kg-1 in tubers was obtained using 138 kg-N ha-1 supplied from urea. The lowest nitrate concentrations came from the nanofertilizer and SCU treatments. In total fertilizer treatments with increased N application, the amount of A-NUE, E-NUE and NUP decreased in both years. The SNNC in the first year and SCU in the second year had the highest E-NUE and ANE of the fertilizer sources. Comparison of means indicated that the highest tuber nitrate in the first year (215.63 mg kg-1) and in the second year (239.7 mg kg-1) was the outcome of the 138 kg-N ha-1 U treatment. Also, the results showed that in the first year, the highest E-NUE was the product of the first nitrogen level of SCU while the lowest levels were produced by the third nitrogen level of urea. It may therefore be concluded that nanofertilizers and SCU are more efficient in cultivations with nitrogen requirements. The changes in the E-PNUE index are similar to those in the E-NUE index. In other words, the values of E-PNUE and E-NUE decrease with increasing fertilizer applications while that of the E-PNUE increases with increasing nitrogen. The potato experiments of Darwish et al. (2006) and Halitligil etal. (2002), showed that E-NUE and E-PNUE were reduced with increasing nitrogen levels of urea treatment. The A-NUE and A-PNUE did not affect yield equally. Also, the role of fertilizers with equal nitrogen in yield increase did not have the same function; nanotechnologies have a synergistic effect on plant response to nitrogen use.
Since a higher yield with the least adverse environmental effects is paramount in the third millennium, it is essential to use suitable fertilizers. The application of nanotechnology to agriculture in recent years has attracted much attention. In this study, reduced nitrate leaching and increased potato yield with an emphasis on reduced soil and water pollution were investigated in experiments with treatments that consisted of three nitrogen levels of NNC, SNNC, SCU, and U. The results showed that the 138kg-N/ha SNNC and 138kg-N/ha NNC produced the highest tuber yield. The lowest yield was obtained from the application of 138kg-N/ha U fertilizer. Comparing N-efficiency parameters showed that the highest E-NUE value was obtained with the first nitrogen level of SCU and lowest was the outcome of the third nitrogen level of Urea. Moreover, comparison of different levels of nitrogen indicated that low nitrogen levels in slow-release fertilizers performed better than the high levels of U fertilizer, which indicates an economic advantage as well. Since this experiment was conducted under greenhouse conditions, repeating this research in field conditions and with other crops is recommended.

In this study, a factorial experiment based on completely in a sandy loam soil under potato cultivation by Hydrus-1D with two factors of nitrogen fertilizer type in 4 levels (Nano- Nitrogen Chelate (NNC), Sulphur Coated Nano- Nitrogen Chelate (SNNC), Sulphur Coated Urea (SCU) and Urea (U)) and nitrogen amount in 3 levels (46, 92 and 138kg-N/ha) was desighned. The results of Freundlich fiting on soil nitrate showed that this model in each three nitrogen levels describes with minimum SSQ NNC and SNNC treatments. The maximum Kdin the 46 kgN/ha belonged to NNC (0.14), in the 96 kgN/ha pertained to NNC (0.17) and in the 138 kgN/ha belonged to NNC and SNNC (0.19). Furthermore, increasing using firtilizer in all treatments led to increasing sorption capacity. The simulation results indicated that the minimum NRMSE=0.07 and maximum R2=0.999 pertained to 138 kgN/ha SNNC. The maximum NRMSE and the minimum R2 belonged to Urea treatment. Overall this study results displayed using nano nitrogen firtilizers leads decreasing nitrate leaching and increasing its keeping in soil.

The objective of this study was to collect all the conducted studies on nitrate concentration in water resources of Iran. To achieve this purpose, the published papers in ISC and ISI journals as well as conferences and seminars were evaluated. The results of this survey showed that 116 studies have been carried out in 26 provinces of Iran. But,there was no published paper in Ilam, Alborz, South Khorasan, Kohgiluyeh and Boyer-Ahmad, and Lorestan provinces. According to these studies, the largest number of studies was performed in Hamadan province (14 cases), Khuzestan, and Mazandaran provinces (10 cases) and the least number of studies was conducted in Ardabil, Bushehr, Qazvin, Qom, and Kermanshah provinces. In Hamadan province, more than 1435 water samples were collected from water resources of this province, which is representative of large number of studies in this region. Maximum nitrate concentration (318 mg/L) was reported in Isfahan province and then in Zahedan city (295 mg/L), Sistan and Baluchestan Province. Based on the reported results in these studies, the nitrate pollution in water resources of Iran is at medium level. In most of the studies, high nitrate concentrations are due to lack of sewage collection network, discharge of urban and industrial sewage to water resources, and agricultural activities, which use high amounts of manure and fertilizer.