سلمان شریف آذری

One of the most important crises that most countries are currently ‎facing is the issue of reducing the quality of water resources. Reducing groundwater resources and ‎increasing pollution, reduced the potential for using groundwater for various uses. One of the main ‎reasons for the decline in groundwater quality is the impact of agricultural drainage due to the ‎excessive use of fertilizers and pesticides. Also, industries that in many cases have contaminated ‎groundwater with chemical and hydrocarbon contaminants.‎‏ ‏In addition to these factors, the disposal ‎of sewage in cities and villages through absorbent wells made up of viruses and bacteria also ‎contributes to contaminants‏.‏‎ Groundwater quality monitoring program can ensure the proper quality ‎of water resources for different uses‏.‏‎ Without monitoring, continuous reporting on the quality of ‎the water supply, its evolution, planning for optimal allocation for different uses, assessing the ‎impact of new developments, and designing and implementing management plans is not feasible.‎‏ ‏Identification of homogeneous regions in terms of groundwater quality in Golestan province of ‎northern Iran using Fuzzy Clustering Method combination with genetic algorithm (GA-FCM) was ‎performed on 14 parameters in a 5- year- time step in 2006, 2011 and 2016.‎

To determine the homogeneous regions for each year, the optimal number ‎of clusters was initially obtained. After data clustering in Matlab software, the results of clustering ‎were evaluated qualitatively with Schuler and Wilcox diagrams. For better representation of ‎homogeneous regions, classification maps for the study area were presented.‎

The results showed that the optimum numbers of clusters in 2006, 2011, ‎and 2016 were 6, 5, and 6, respectively. Analysis of groundwater quality classification maps ‎showed that in 2006, cluster no. 6, including 2.7% of the studied wells located within the city of ‎Kalaleh, is poor in terms of drinking and farming groundwater quality. Also, based on the results, it ‎can be seen that 36.8% of the wells across the province were in good condition in terms of quality ‎of drinking and agricultural parameters in 2011. Likewise, 33.33% of the wells are in a moderate ‎condition in terms of drinking quality, and the status of their groundwater has improved in terms of ‎quality since 2006. Also, the results of NSGA- FCM in 2016 showed that most of the parameters ‎‎(5.55% of the wells in the province) in the cluster 3 have a moderate quality.‎

The findings of this study showed that the groundwater quality in the province in ‎‎2016 is lower than in 2011, so appropriate management plans should be adopted. Moreover, it was ‎observed that the fuzzy clustering method is a suitable method for assessing and identification of ‎critical region of the quality of groundwater resources, since it considers the uncertainty conditions ‎in the classes of the classification system.‎

Correct estimation of the river sediment volume is important for many water resources projects.In this study, the empirical equations of sediment transport, support vector regression (SVR), and developed k-nearest neighbor regression (KNN) were used in order to estimate suspended sediment load in the Sistan River. In this regard, in addition to the maximum temperature, minimum temperature, and dischargeand Suspended Loud In the period 1374 to 1390, it was paired on 1682, discharge, the classified discharge was used as effecting variable to suspended sediment load modeling. For each of the input combinations in support vector regression and development k-nearest neighbor regression the best structure of the regression model determined based on the performance criteria. The Result showed that the Toffaleti method is the best method between empirical equations with R2 equal to 0.705 and RMSE equal to 66558 Ton/day. Also, the support vector regression model with discharge, minimum temperature, maximum temperature and classified discharge as the set of input data is the best model in estimation of suspended sediment load with R2 equal to 0.96 and RMSE equal to 2809.3 Ton/day. The results indicate that the regression method estimate suspended sediment load much better than empirical equations in the Sistan River.

One of the most important processes of erosion and sediment transport in streams is the river most complex engineering issues.this process special effects on water quality indices, action suburbs floor and destroyed much damage to the river and also into the development plans Lack of continuity sediment sampling and measurement of many existing stations. due to the low number of hydrometric stations in Iran and the lack of continuity of sediment sampling and measuring in many existing stations, on one hand the exact amount of sediment load in many rivers in the country is not available and because of differences in climatic, hydrological and topographical conditions in the country, on the other hand, the preparation and calibration of sediment Erosion Models different regions, is unavoidableCalibration models of erosion and sedimentation in different locations is difficult and requires financial capital andthe time . the But evolutionary optimization algorithm able to resolve this problems of mathematical and experimental methods in this paper, a new optimization algorithm spiders can be made to education And the evolutionary pattern for input (discharge and precipitation) and rain-gauge gauging stations and Watershed Kardeh designated evolutionary algorithms and artificial network performance for 24 year 24-year dam catchment Kardeh for the period studied. In conclusion, the results proved that social spiders optimization algorithm t better resultspredic to for sediment in watershed Kardeh

Sedimentation on the rivers beds causes many difficulties not only for an orderly and sustainable flow of water, but also by altering the river's hydraulics, which in certain cases drastically changes the flow direction by opening new channels that inundate farm fields, municipalities and infrastructures. As various models developed for the suspended load (SL) estimation are often site specific, therefore, it is prudent to test and calibrate them for the intended river. As the Hirmand River is the lifeblood of Sistan, therefore, the 70 km terminal reach, the Sistan River, was selected for this project. The monthly and seasonally discharge of the river and its SL contents for the period 1970–2015 were analyzed statistically using six models, namely: linear, double-linear, mean value, FAO, parametric factor and non-linear. As the determination of the break point is very important in the application of the double linear method, the genetic optimization algorithm was used in this process.Two criteria for choosing the correct method for estimation of the SL were used: relative mean error and root mean square error. It is concluded that the double linear method proposed in this research gives the best result in all forms without any delineation, seasonal and monthly classification with the relative mean error of 154.46, 142.7 and 116.15, respectively. Monthly classification of data increased accuracy and reduced the root mean square error in the Sistan River

In the wake of the damage that man has Sntgrayyash the ground , place the study of climate change and global warming , due to the industrialization of the earth , the day will be added .The radiative effect of greenhouse gases on global warming is a major factor and the factor in global climate change with unprecedented speed . Every kind of climate change on the terrestrial earth is the beginning a chain of reactions whose direct effect on hydrological processes can be observed.
In this study, SDSM model is used to measure the large -scale data of the atmospheric general circulation model (HadCM3) in two local climate change scenarios, A2 and B2, for the meteorological parameters of temperature and precipitation in the basin of Qara Su. Then, considering the data of rainfall, temperature and flow rate and the fine- scale model outputs exponentially in the an artificial neural network is used to calculate the river’s discharge during the future period. Climate model results indicate an increase in temperature and a decrease in rainfall within the desired course relative to the base period. Consequently, this increase in temperature and decrease in rainfall will lessen the discharge rates of Qara Su River.

Every kind of climate change on the terrestrial earth is the beginning of a chain of reactions whose effect can be directly observed on hydrological processes. Qara Su basin catchment, located in Kermanshah Province, is one of the important sub – basins of Karkheh River that, in terms of agriculture and food security, is considered as an important area in the west of Iran. In this regard, achieving reliable methods of predicting the river flows in order to plan proper operation of water resources has become increasingly significant. In this study, first, Statistical Downscaling Model (SDSM) was used to measure the large -scale data of the atmospheric general circulation model (HadCM3) in the local climate scenario, A2, which considers both the rapid economic development for the future world, the remarkable technological advances, and excessive use of the fossil fuels in future. These are measured for the meteorological parameters of temperature and precipitation in the basin of Qara Su. Then, considering the data of rainfall, temperature and flow rate during the baseline (1971-2000) and the outputs of the fine- scale model in the period (2011-2040) as well as using an artificial neural network in MATLAB 2012 the river’s discharge during the future period was simulated. Ultimately, the allocation condition of water resources and the required water for agricultural sector were simulated using the water evaluation and assessment programming model, WEAP. The results of the climate model indicated a rise in temperature and a decrease in rainfall within the desired course rather than the base period (1997-2000), so that it was followed by a 1.6 º C rise in temperature and a 1.77% decrease in rainfall all over the area. Consequently, after this increase in temperature and decrease in rainfall, a 32.62% decrease of discharge rate is estimated for Qra Su River. The result analysis of WEAP model, under the climate scenarios and given the existing under- cultivation level, indicates an increased unmet need for agriculture in the studied region for future period.

Water quality is one of the most affective factors on soil infiltration. The widespread use of wastewater in recent years caused the accurate estimation of soil infiltration to be essential for achieving proper irrigation management. Therefore, Philip, Kostiakov, Kostiakov-Lewis, Green-Ampt,Horton, SCS and linear regression models for soil infiltration estimation were evaluated. Infiltration experiments were carried out in an area of 190 ha of agricultural lands under wheat cultivation, around the Zabol refinery of municipal waste water for six soil texture classes include silty clay, clay loam, sandy loam, loam, loamy sand, silty clay loam. These lands are irrigated with treated wastewater for over 10 years. The study area was divided into pixels with 150mÍ150m size. The cumulative infiltration was determined using double ring experiment. Some physicochemical soil properties were determined in soil samples taken from the center of each pixel. Despite of significant relation between soil infiltration and EC, sand, clay and silt percentage, exchangeable sodium and bulk density, transfer functions didn’t led to satisfactory results for cumulated infiltration. Kostiakov and Horton models were the best profitable models for silty clay soils. Whereas, the others were the best for cumulative infiltration estimation in silty clay loam soils. Green-Ampt and Kostiakov models were the best fitted on the observed cumulative infiltration data with root mean square, mean bias error and efficient coefficient of 0.16 and 0.15 cm, -0.03 and 0.04 cm, and 0.74 and 0.78 cm, respectively. The considerable reduction in soil infiltration due to over accumulation of salt and sodium in the surface soil layers through long term application of waste water in the study area.

Stability of catchment with stability criteria in different ways and by many researchers has been estimated. Inclouding Water poverty index (Sullivan, 2002), Canadian water sustainability index by Policy Research Initiative, Watershed sustainability indecies (Chavez and Alipaz,2007) (15,4). Integrated water resources management in large scale basins regard to aspects of economic, social and environmental criteria for water resource planning is essential(3). This type of management with DPSIR framework takes executive shape. Therefore, in this study, Integrated Water Resources Management based on calculating of sustainability indices in the economic, social and environmental terms of DPSIR framwork were evaluated. This framework includes Driving Force- Pressures-State-Impact-Response terms. Driving forces in the form of three scenarios such as continue the status quo, optimistic and pessimistic; have been assessed in Mike Basin. The status quo scenario takes into account the current situation for the future of the basin and river discharge time series and demands for domestic, industry and agriculture in the future, such as the status quo. The optimistic scenario creates a positive climate change in the region, increasing water availability and constant water demand consideration. In this scenario river flow in the scenario of continuing the status quo as much as 6.7 percent and the rate of temperature, rainfall and agricultural levels constant, such as the status quo scenario. Pessimistic scenario represents a reduction of available water resources and increasing demand (16,19). By executing each scenario, system resources and demands, including allocating and deficit change the state of the system and the simulation results of mike basin inter to excel and assess the effects of implementation of each management options with calculating indecies in mike basin-excel decision support system helps decision makers and Stakeholders to Decision-making and policy-making. Multi-criteria decision-making methods to solve the problems with having a three-dimensional matrix include scenarios, options and indecies is essential. One of these methods of ranking of options is based on the distance of ideal point is "TOPSIS" method (18). TOPSIS method for evaluation of quantitative and qualitative indicators matrix in scale of spatial map by GIS software indicated that the Avagholi district, has the good condition in sustainability but also, the places located in the eastern of the Aras basin such as Ardebil, Meshkin Shahr, Ahar-Varzaghan, have low sustainability than the whole basin. To improve the state of the system, in the Response parameter, three structural alternatives is defined for demand management. The results of the MCDM method show that the option of "increase water efficiency" is better than decrease cultivated area and canal maintenance in all scenarios. In this study, combination of quantitative and qualitative indicators has assessed the sustainability of thirtheen areas of Aras catchment within the "DPSIR" framework was done. Based on this framework five factor of driving force- Pressure- State- Impact- Response defined to Mike basin water resources simulation model. The out put of this model is intered to excel to calculate indicators. Based on TOPSIS multicriteria decision making that is useful to solve three dimensional matrixes of scenario- alternative- indecies, the east area of Aras basin has less sustainability than the others. Duo to alternative evaluation the increase of water efficiency help watershed sustainability based on indecies defined to the research area.

One of the water resources modeling requirements is sufficient knowledge of long-term series of meteorological and hydrological parameters. In this study the nearest neighbor resampling method presented by Lall and Sharma was developed. In the developed model، the KNN regression was used for time series forecasting instead of local polynomial used in the developed algorithm by Prairie. In this case problems caused by polynomial degree estimation were solved. This caused that multivariate time series simulation became feasible. To evaluate the performance of the developed model recorded data in the Kawtar hydrometer station located on one of the main branch of Mahabad River and Mahabad synoptic station were used. The results from time series simulation showed that the developed model is able to keep important statistical characteristics of historic series. In addition، it could solve the classic nearest neighbor resampling method problem in order to produce values not seen in the historical record. Also، this model showed that it could simulate the multivariate hydrologic series.