kumars ebrahimi

Rivers, as vital water resources, play an essential role in maintaining the health of the environment and living organisms. The rivers bordering the Caspian Sea are recognized as the most accessible surface water sources and, unfortunately, are often utilized as dumping grounds for pollutants. These rivers have a close connection with populated areas and economic activities, which is why the management issues related to them have significantly escalated. The quality of river water directly affects the health of humans and aquatic ecosystems. Pollution resulting from human activities, such as wastewater discharge, can lead to serious health and environmental problems. For example, research by Rostami and colleagues has demonstrated the negative impact of urban wastewater on the water quality of the Kanger River, which is also true for the Talaar River. These challenges necessitate multifaceted management and scientific approaches to develop protective strategies. Golamian and Yildirim's studies highlighted the impact of land-use changes on the intensity of floods and erosion, emphasizing the need for a detailed analysis of these changes. This research examines the damages inflicted on the Talaar River and provides a framework for analyzing similar hazards in other rivers across the country. The findings of this research underscore the necessity of collaboration among government agencies, local authorities, and civil society to create effective solutions for the sustainable management of water resources. By gaining a deep understanding of the existing challenges and adopting comprehensive management practices, we can help protect these valuable resources and improve the overall quality of public life.

The WRASTIC index is a systematic approach for assessing the susceptibility of watersheds to polluting surface water resources, based on key characteristics of the watershed and land use within a hydrogeological environment. This method became popular starting in 2000 and is considered one of the most comprehensive risk assessment methods for surface waters when compared to other techniques. In this approach, several factors within a watershed are meticulously evaluated to determine the sensitivity to surface water pollution, including wastewater discharge (W), effects of recreational land use (R), effects of agricultural land use (A), watershed size (S), transportation pathways (T), effects of industrial land use (I), and the extent of vegetation cover (C). The numerical values assigned to the aforementioned parameters in the WRASTIC model are used in equation to calculate the WRASTIC index value for a given watershed:
WRASTIC Index=WrWw+RrRw+ArAw+SrSw+TrTw+IrIw+CrCw
In this equation, the index (r) is used to assign scores to each parameter, while the index (w) represents the weight of the parameter. The values of (w) range from 1 to 4, and the values of (r) range from 1 to 5, with the industrial parameter being assigned a range of 1 to 8. According to the above equation, the higher the numerical value of this index, the greater the potential for pollution within the watershed. It is worth noting that for a better and more accurate assessment, the input of professionals should be utilized.

The results of the WRASTIC index assessment for the watershed of the Talaar River indicate a medium sensitivity to pollution, with a score of 5.26. This score raises concerns regarding the influx of urban and industrial wastewater, agricultural runoff, and the discharge of sewage from soakaway pits. These pollution sources pose a significant risk of severe contamination to the river, leading to consequences such as eutrophication and a decline in water quality. Furthermore, the increasing population in areas near water resources has led to land-use changes, resulting in a higher runoff coefficient and intensified flooding occurrences. These conditions may cause serious damage to infrastructures, natural ecosystems, and public health. Another weakness of the WRASTIC index is the neglect of the status of wildlife and natural habitats that are at risk, mainly due to deforestation and poaching. These issues not only threaten the biodiversity of the region but also adversely affect the ecological balance and water quality. Therefore, there is a clear need to establish a comprehensive and new framework for more accurately assessing pollution levels and maintaining ecological balance in this area. Making informed decisions and implementing proper management practices can improve water quality and contribute to the protection of the environment and biodiversity. The sustainable management and preservation of water resources and habitats require serious attention and collaboration from all stakeholders to make the best use of these sensitive and valuable ecosystems.

Crisis management and analysis of existing challenges require precise identification of damaging factors and the development of a comprehensive framework to explain the relationships between these factors. Such a framework can assist in classifying the damages incurred and improving risk management and crisis response. By examining and analyzing the damages from both temporal and spatial perspectives, a comprehensive crisis management approach can be realized. Thus, in the Talar River watershed, the presence of multiple challenges, including declining water quality, changes in hydrological regimes, alterations in adjacent land use, disregard for riverbed and buffer zone protections, illegal constructions, soil erosion, and excessive extraction of materials, necessitate the identification, management, and prioritization of existing issues to safeguard water quality and ensure environmental sustainability.

Modeling is one of the most effective methods in river water quality management, planning to reduce the entry of pollutants and control environmental damage. In this article, the water quality of Babolrood river using the Qual2kw model and meta-heuristic model of support vector machine and SVM-GA genetic algorithm with the approach of simulating six water quality parameters including temperature, alkalinity, electrical conductivity, total nitrogen, dissolved oxygen and biochemical oxygen demand in four time periods from 2018 was investigated. Genetic algorithm is used for simulation and optimization, and the hybrid of this model with support vector machine has improved the simulation performance. In order to compare the observed and simulated data, the explanation was used coefficient R2 of the average absolute error MAE and the root mean square of the normalized error NRMSE. The results showed that the best SVM-GA simulation for total nitrogen parameter in February is with MAE 0.15 and NRMSE 1.97. Also, the results of Qual2kw numerical model showed that the best simulation for May is with MAE 0.14 and NRMSE 1.2. The water quality of Babolrood River was evaluated using the NSFWQI index. Based on this, the best water quality is in the month of Bahman with an index of 56 in the medium to good range and the worst quality is in the month of May with an index of 49 in the bad range.

Predicting water quality parameters plays a crucial role for monitoring of ecosystems of rivers and their sustainability. Alongside this, conventional prediction models are not able to capture the non-linearity and non-stationary inherence of water quality datasets. In recent years, the rapid development of machine learning methods has transformed the water quality prediction fields. In this study, water quality parameters for the Dinachal River in Guilan province have been assessed and predicted. Two models based on Artificial Neural Networks (ANN) and Support Vector Regression (SVR) were utilized to predict nine water quality parameters as TDS, EC, pH, Cl, SO4, HCO3, Ca, Mg, and Na with monthly timesteps between 2006 and 2018. Then, the model’s performance was evaluated using RMSE, MSE, and MAPE indices. According to the results, the SVR model was superior in predicting TDS and Mg parameters with an RMSE Index of 2.03 and 0.062, respectively. Simultaneously, ANN had a slightly better accuracy in the prediction of remaining parameters. However, prediction results for both models in the case study were satisfactory. In addition, the SVR model predicted TDS with a MAPE of 0.007, which was the best compared to other parameters. At the same time, the ANN model had better performance in predicting EC with a MAPE of 0.001. Prediction results for Cl had also the lowest accuracy among water quality parameters with an RMSE of 0.055 and 0.052 for SVR and ANN, respectively. Methods utilized in this study can be effective in predicting water quality parameters of Dinachal river.

Rivers play a crucial role as a primary water source for societies, emphasizing the need for continuous monitoring of their quality. In this article, we focus on the assessment of water quality in Dinachal River, located in Gilan province. The evaluation was conducted through field studies, sampling, and laboratory methods. Specifically, we measured and analyzed five hydraulic parameters and thirteen water quality parameters, including nitrate, phosphate, temperature, and acidity. The study encompassed seven selected sections along a 25 km stretch of Dinachal River in September 2021. To analyze the data, we employed various methods such as Schoeller, Piper, and Wilcox, along with the FAO and WQI indices. Spatially comparing the river water quality revealed a consistent increase in most parameters from the upstream section, with a steeper rise observed from the station near SafarMahaleh village (section4) towards the river's end. Notably, three parameters electrical conductivity, nitrate, and total dissolved solids experienced significant increases. Electrical conductivity rose from 315.67 µS/cm to 712 µS/cm, total dissolved solids increased from 202.03 to 455.68 mg/l, and nitrate levels elevated from 11.27 mg/l to 69.47 mg/l along the river. Conversely, nitrate levels rose from 19.4 mg/l to 21.4 mg/l, and electrical conductivity increased from 328 µS/cm to 416µS/cm. When comparing data between 1996and2016, specifically for the months of July and June, we noted that agricultural drains had caused nitrates to exceed the permissible limit. The findings indicate an overall deterioration in certain parameters, particularly in relation to electrical conductivity, nitrate levels, and total dissolved solids. These results emphasize the need for effective measures to mitigate pollution sources and preserve the river's water quality for the well-being of the surrounding communities.

Herein, the stability of the Zarandieh-Saveh aquifer was investigated, based on field monitoring data and simulation. It was simulated involving GMS software and calibrated and vitrificated in both steady-state and unsteady-state conditions, for 84 monthly steps(2012 to 2022). The values of Aquifer Sustainability(AS) and Groundwater Dependence(DG) indices calculated equal to 31.1% and 0.78%, respectively, which shows the aquifer is in a critical condition and the region is highly dependent on groundwater. According to the Groundwater Damping Index(GDR), with the continuation of the present extraction process, the aquifer will be completely discharged in the next 53 years. The scenario of 20% reduction in annual extraction as the most appropriate scenario will result a rise equal 4 meters in ten years. The operation of the seasonal floods artificial recharging project has had a significant effect on improving the level of this aquifer. The interbasin transfer and recharging scenario of Al-Ghadir Dam overflow have a positive and significant effect on the groundwater level equal to 0.75 meters per year. Based on the results, the parameters and factors affecting the balancing of the Zarandieh aquifer, the first priority is to reduce the exploitation and then increase of recharging from the Al-Ghadir dam overflow site, which can have significant effects on the stability of this aquifer.

Irrigation networks are the main infrastructures for water conveyance and distribution in agricultural sector. Chemical damage of concrete is one of the main limiting factors for service life of irrigation networks. The main target of this research was to investigate and compare the risk of chemical detriments on concrete structures of Golestan and Kausar-Nomal irrigation networks. In this regard, in June 2022, water samples were collected from different points of the studied networks and the laboratory tests were performed. Then, the results of the water-quality parameters in tandem with the previously available monthly water quality data of the studied networks, were analyzed based on credible international criteria and using water corrosiveness indices. The Ryznar index values, for both Golestan and Kausar-Nomel networks, during October 2021-June 2022, were beyond the threshold of water corrosiveness (6.8). The average values of Ryznar index for the water samples of Golestan and Kausar-Nomel networks were 7.45 and 8.15, respectively, which means the corrosiveness intensity of Kausar-Nomel network’s water is significantly higher. Also, the concentration of dissolved-CO2 in the water of Kausar-Nomel network, in June 2022, was equal to 31.7 mg/l, which means that there has been a risk of cement decomposition. On the other hand, during the nine studied months, the sulfate-ion concentration values in the Golestan network’s water were recorded more than 150 mg/l, which is more than the minimum required for reaction with concrete. In four months out of the nine studied months, the magnesium-ion concentration values in the Golestan network’s water were more than 100 mg/l which are the beyond of threshold risk in concrete chemical damages.

The main purpose of this research was to investigate the water quality of Zarrinehrood and Siminehrood rivers for the year 2018 and in 13 and 6 stations of the two rivers, respectively. For this purpose, the unpublished data of Na+, Cl, SO4, TDS, pH, CO3, HCO, EC, Ca, Mg and SAR parameters from both rivers were used and presented in this paper. Graphical methods were used to determine the water quality of the rivers for various human uses based on drinking and agricultural standards, and to determine the water quality in terms of sustainability, Langelier, Ryznar and Pukorius indices were used. Also, the water quality conditions of the studied rivers were compared and studied with the national standards of Iran (1053), the World Health Organization, the United States, the European Union, and the water quality standards of Iran of the Environmental Protection Organization. The results indicate that the type and facies of water is calcium bicarbonate in study stations and water quality of most stations are suitable for drinking and agricultural usages. The SAR value in Shirin-Kond station is 1.51 mEq/L and the EC value is 1144 mmho/cm and its water quality is in class C3-S1 that can be used for agricultural purposes. According to the Results, water quality of studied stations is corrosive for industrial uses.

It is necessary to use the correct methods of water economic valuation. Proper use of the virtual water concept and the crop pattern can play a significant role in increasing water productivity and sustainable development.In this paper, the economic value of agricultural water, once without considering virtual water and once again with virtual water for four crops including wheat, barley, alfalfa and fodder maize was calculated, using a native-exclusive mathematical model, in Isfahan province, counties of Shahinshahr-Meymeh and Borkhar, Iran. Three weighting methods based on cultivated area, volume of net water consumption and product income were considered for water valuation. The input data for the mathematical model includes crop parameters, meteorological data, the amount of water consumed for each plant and the amount of expenses and incomes from each product for the crop year 2020-2021.The obtained results showed that by all the three weighting methods and in the both studied areas, the economic value of agricultural water increased by considering the virtual water of crops and changing the crop pattern accordingly; the water economic value by three weighting methods based on cultivated area, the amount of water consumed, and crop income increased by 22%, 44%, and 23% in Shahinshahr-Meymeh county and by 17%, 15%, and 15% in Borkhar county, respectively. This research showed that considering virtual water and changing the crop pattern accordingly can increase the economic value of water, reduce water consumption and help achieve sustainable development.

Understanding flood phenomena and estimating flood potential play an important role in flood management and forecasting in any region. Due to the flooding of Mahneshan-Angoran basin, located in Zanjan province, Central Iran, in this study, the effective factors on flooding of this basin were investigated. For this purpose, first the basin curve number map was created by combining soil maps, land use, Digital Elevation Model and Look up table involving GIS environment. Then, using AHP Hierarchical Analysis method and based on the combined effects approach, appropriate weights were assigned to each of the slope factors, slope direction, land use, curve number, branch ratio, drainage density, precipitation, basin length and basin lithology. A flood risk map was prepared from the overlap of the weighted layers in the GIS environment. The results of AHP method showed that the highest weight among the effective factors is equal to 0.326, which belongs to the factor of the basin Curve Number. Other effective factors are: land use, slope, rainfall, drainage density, branching ratio, basin length, slope direction and lithological factor, which respectively have weights affecting flooding equal to 0.174, 0.142, 0.118, 0.090, 0.051, 0.042, 0.042 and 0.016. According to the obtained flood plan, it was observed that the western parts of the basin have a higher risk than other parts of the basin due to higher altitude and more rainfall, which is itself a proof of the accuracy of the calculations.

This study was aimed to investigate the risk of chemical damages originated from water quality on concrete structures of Voshmgir dam and its irrigation network. In this regard, field survey and water sampling from the dam and its network were carried out in June 2022. In order to determine the intensity of water chemical aggression to concrete, the results of water quality tests were analyzed using soft water aggression indices and well known international standards. Also, the temporal changes of water chemical aggression was investigated using the data received from Golestan water authority. Langelier and Ryznar indices for dam water in June 2021 are -0.6 and 8.6, respectively, and based on these indices, the dam water is corrosive and very corrosive, respectively, and the concrete structure of  spillway is exposed to severe soft water attack. In all studied months, there has been aggression risk of at least one damaging agent to concrete. In December 2021, Ryznar index was 8.49 and the amounts of sulfate and magnesium were 400 and 199 mg/liter, respectively, and there has been a risk of simultaneous aggression by three factors of soft water, sulfate and magnesium. Assessment of water quality of Voshmgir dam in five months showed that the water is corrosive in four months and there is a risk of sulfate and magnesium ions reaction with concrete in two months. So, to protect the spillway’s concrete structure and lining of irrigation canals against leaching by the corrosive dam water and damages originated from reaction of sulfate and magnesium ions with concrete, utilization of epoxy coatings is suggested.

In the face of the critical situation of groundwater resources in Rafsanjan Plain, the question arises: Why have none of the policies implemented in order to preserve groundwater resources had a positive effect on controlling the decline of the groundwater level? An analysis of the water problem in this plain was conducted as an introduction to provide water policy in this area taking into account the hypothesis that the problem recognition has not been properly done. It has been explained using the approach of Grounded Theory for data analysis and the framework of Path Dependenty using causal loop diagrams as analytical and graphical tools. Study results show that historically, all factors have contributed to the development of pistachios to become a strong economic stimulus and affect increasing water consumption. The problem of water scarcity was also seen only as a physical constraint, and the measures taken were only targeted to overcome this limitation. It was generally identified that the current state of Rafsanjan groundwater resources is strongly related to the historical path of development of the pistachio crop, and that reforms and changes to water institutions will be more cost-effective in following this path. Furthermore, policy recommendations are provided in detail.

The purpose of this study is to investigate landuse changes in Gorganrood basin in 2001, 2010 and 2019. Using Landsat and Product-Modes satellite images, used maps were prepared using the classification method of random forest algorithm in Google Earth Engine. Satellite imagery was classified into eight classes including forest, cropland, shrubland, grassland, wetland, urban, barren, and water. The results showed that the highest landuse area in 2001, 2010 and 2019 is related to cropland, respectively, equal to 3150.58, 3403.8 and 3031.82 square kilometers, and lowest amount in 2001 is related to water with an area of 84.67 and in 2010 and 2010 is related to wetlands with an area of 167.98 and 118.7 square kilometers. Comparison of the results showed that the urban landuse study period in 2019 compared to 2001, it has grown by about 34% (341 square kilometers). Also, forest lands at a rate of 5.5 percent (148.92 kilometers), shrubs at a rate of 8.7 percent (135.79 kilometers) and arable land at a rate of 3.8 percent (118.76 kilometers) had a negative growth.

Global warming has changed rainfall patterns and reduced snow sources. The main objective of this research was to investigate the impact of temperature increase on snowmelt and river runoff in hot months of the year along with analysis of climate variables in Tamar basin, Iran. For this purpose, the snow cover area was extracted from the daily images of Modis Satellite, based on elevation. Then, the study area was divided into four districts. Discharge flow measured data of Tamar hydrometric station, precipitation of Qarnaq and average temperature of Gidagh stations were used from 2013 to 2016 for calibration and from 2017 to 2019 for verification, to simulate snowmelt runoff. Snow cover extent, in calibration stage and january, reached the highest values of 28% and 28.8% in the third and fourth districts, respectively. Furthermore, in the verification stage, the third and fourth districts in February had the highest percentages of 45.8 and 30.2, respectively. Comparison of simulated and measured runoff data revealed that the portion of runoff due to snowmelt is significant by passing from February to April. So that the highest increase (from 1.8% to 39.1%) in runoff amount was corresponded to the water year of 2018-2019. The highest and the lowest percentages of snowmelt in water years of 2014-2015 and 2017-2018 were 19 and 3.3 percent, respectively. The results of sensitivity analysis of model parameters including temperature decrease rate, critical temperature, delay time, degree-day factor, X and Y coefficients, precipitation runoff coefficient and snow runoff coefficient showed that the rainfall coefficient and X parameters are the most effective parameters. The effect of temperature and rainfall on runoff process was varied in different months.

The amount of Total Dissolved Solids (TDS) is an important factor in determining the water quality of rivers. The purposes of this paper was to investigate the efficiency of four intelligent models including, SVM-IWO, SVM-PSO, SVM-ABC, LS-SVM and Bayesian Neural Network, in predicting water quality of Babolrood and Sefidrood rivers, north  of Iran. To achieve this aim, unpublished measured monthly data including; Ca, Mg, HCO3, Na, SO4, EC, pH, and TDS from Quran-Talar station of Babolrood river and Prorijabad station of Sefidrood river were analyzed in the periods of 1966-2015 and 2002-2015, respectively. Evaluation of the four above-mentioned models, based on the correlation coefficient (R2), Root Mean Square Error (RMSE) and the Nash-Sutcliff coefficient (NSE) showed that the SVM-ABC model in both Babolrood and Sefidrood rivers with the highest R2s equal to 0.985 and 0.989 and the least amounts of RMSE equal to 10.493mg/l and 5.289 mg/l and the highest Nash-Sutcliffe equal to 0.983 and 0.992, respectively, has better and faster performance compared to others models in assessing water quality.

Surface water, especially rivers, is one of the most important water resources that play an important role in supplying water for various activities. The purpose of this study was to investigate the temporal and spatial variability of water quality parameters in three different basins, in terms of land use, at 50 hydrometric stations in 9 rivers in the period 1992-2015 by using multivariate and GIS statistical methods. In this study, factor analysis based on 10 qualitative parameters was performed to determine the most important parameters affecting the surface water quality of the study area. The results of Principal component analysis showed that the North, Northwest and Southwest basins have two significant main components with 82.38%, 79.86% and 81.60% of the total variance, respectively. Cluster analysis of hydrometric stations located in northwestern and southwestern regions was divided into two clusters and north into three clusters. In the cluster analysis, some stations in Aji Chay, Atrak and Zayanderood rivers had different water quality characteristics than other stations. These stations are located downstream of the rivers. Based on the pattern of mean water quality parameters in GIS and land use map, station 2 had the lowest values for most parameters.

Rainfall-runoff monthly modelling process plays an important role in dams’ operation. Herein the performances of three data-based models including Artificial Neural Network (ANN), Generalized Regression Neural Network (GRNN) and K-Nearest Neighbor (KNN) are compared in tandem with IHACRES conceptual model, while they were applied with similar data, and optimal structures. Simulation of monthly inflow to Karkheh reservoir, Iran, was considered as the case study, and 32-year data (1982-2014) of monthly temperature and precipitation belong to the upper sub-basin of the dam, and monthly inflow to the reservoir were used. With respect to the different rainfall-runoff patterns in different months, the models assessed in a general and monthly manners using a rating method based on performance criteria including: Nash-Sutcliff Efficiency (NSE), Root Mean Square Error (RMSE) and Correlation Coefficient(R). Results showed that both model evaluation procedure in validation phase, ANN and KNN models have the highest and lowest efficiency in monthly streamflow forecasting, respectively. Based on the rating general evaluation the performance of ANN (NSE= 0.749, R= 0.868) and IHACRES (NSE= 0.699, R= 0.842) are similar with a score of 8 while the GRNN (NSE= 0.618, R= 0.793) and KNN (NSE= 0.601, R= 0.777) models with similar performance (score 5) were ranked in the second order. However, in accordance with rating monthly assessment of the models, the performance of GRNN was similar to IHACRES with the total score of 38 based on three criteria while they were ranked in the second order after ANN model with score 48.

The Inter-basin water transfer is one of the ways to deal with water shortage. However, in some cases, this solution has more destructive effects than its benefits. Therefore, it is necessary to fully identify the potential damages of inter-basin water transfer projects, so that they can be prevented in future plans. Iran is one of the leading countries in inter-basin water transfer projects and so far, 10 projects are under operation and 19 more are on the agenda. The main purpose of this research study is to identify the damages of projects in operation in Iran. To achieve this aim, all information related to the in-operation projects were collected and extracted involving descriptive-analytical approach, from literature resources, news and interviews. A total of 20 damages were identified as a result of the investigations. Due to the different nature of these damages, after identification, they were classified into four areas including, environmental, economic, political and social. According to the results, there were only 10 damages in the research literature, which means that the literature is incomplete in relation to the damages of inter-basin water transfer projects. It is recommended that the results of this research be used in the study of future plans.

The catchment area of ​​the Minab river basin and its main branches, as a part of Makren coast, southern Iran, are one of the important areas for water resources planning and management. The aim of this study was to set-up a comprehensive water resource planning and management model, involving WEAP software, for the area. The area includes the Minab, Jeghine Toukahoor, Manoojan, Roodan, Nodej, Dehkohan, Mosaferabad and Faryab Galashgerd. Two scenarios including: (RS) as the reference scenario, and a conservative agricultural development (CAS) were implemented for the future horizon of year 1410, using WEAP model. Then the outputs were evaluated using the performance criteria including; volumetric reliability, reversibility, vulnerability and stability indices. A 15-water year data (2000-2014) was used, 10 years of that for calibration purposes and the rest of 5 years data for verification. The values of evaluation indices, coefficient of determination and Nash-Sutcliff coefficient, for calibration period were equal to 0.98, 0.97 for the Esteghlal dam volume and 0.98 and 0.98 for the hydrometric stations, and 0.90 and 0.86 for the groundwater aquifers, respectively. On the other hand, for the verification period, the values of the above-mentioned indices were equal to 0.98 and 0.98 for Esteghlal dam volume, 0.78 and 0.70 for hydrometric stations, and 0.97 and 0.88 for the groundwater aquifers, respectively. The above-mentioned index values indicate that the modelling results are acceptable. Moreover, between the two considered scenarios, the scenario of conservative agricultural development, with a sustainability index of 55.47%, has a higher priority.

In this study, the performance of Support Vector Machine (SVM) and Radial Base Neural Network approach in predicting the water quality of SiminehRood River was examined. For this purpose, the Sodium Adsorption Ratio (SAR) and Chlorine ions were considered as indicators of water quality in agricultural use. Sodium, calcium, magnesium, pH, Ec, and river flow rate were utilized as input monthly parameters throughout a 12-year period (2003-2014). The results evaluated based on correlation coefficient, root means square error and mean absolute error. The results of the validation period in 4 stations of Pol Bukan, Dashband Bukan, Ghezel Gonbad and Kaullan showed that the SVM model in comparison with the neural network of the radial base function, has higher correlation coefficient (SVM: 0.71 to 0.94, RBF: 0.3 to 0.5), the lowest root means square error (SVM: 0.028 to 0.075 mg/l, RBF: 0.0672 to 0.317 mg/l), a lower absolute mean error (SVM: 0.003 to 0.033 mg/l, RBF: 0.087 to 0.19 mg/l) for the chlorine ion parameter and in the same order SVM values: 0.63 to 0.88 and RBF: 0.21 to 0.38, SVM: 0.0013 to 0.0282 mg/l and RBF: 0.047 to 0.025 mg/l, SVM: 0.0085 to 0.046 mg/L and RBF: 0.0653 to 0.0996 mg/l for sodium absorption ratio.Therefore, the Support Vector Machine model has better accuracy and performance for predicting water quality parameters of SiminehRood River than the Radial Basis Function Network.

Due to the lack of hydrological and meteorological stations, the use of data-based models is essential. Herein efficiency of Gene Expression Programming Models and Support Vector Machine are evaluated involving peak flood discharge prediction of Mahneshan-Angoran basin as a case study, central Iran. For this purpose, observational data of 36 annual maximum daily flow (1975-2011), corresponding rainfall and average monthly temperature of three stations including Mehrabad, Yangikand and Qarahgoni were used. The observed and predicted peak discharge flows in both models were compared based on the RMSE, explanatory coefficient (R^2) and Nash-Sutcliffe (NSE) criteria. The mean values of RMSE in the validation stage for the GEP model in Yingikand, Qarahgooni and Mehrabad stations are equal to 0.049, 0.080 and 0.050, respectively, and in the training stage are equal to 0.042, 0.060 and 0.047, the mean values of R^2 in the validation stage in above mentioned stations are equal to 0.88, 0.86 and 0.87, respectively, and for the training stage estimated equal to 0.89, 0.89 and 0.92. NSE values in the validation stage is equal to 0.75 for all three stations. However that is equal to 0.77, 0.76 and 0.80 for the mentioned stations in the training stage. Also, the RMSE values in the SVM model for the validation stage are equal to 0.042, 0.040, 0.054, respectively, and in the training stage are equal to 0.053, 0.064 and 0.044. R^2 values in the validation stage are equal to 0.66, 0.85 and 0.73. Also for the training stage are equal to 0.86, 0.88 and 0.91. NSE values for validation are equal to 0.56, 0.75 and 0.61 and for the training stage are equal to 0.71, 0.77 and 0.80. According to the evaluation criteria, the GEP model performed relatively better and this model is more suitable for predicting floods in Mahneshan-Angoran basin.

Stream flow forecasting on a monthly time scale is essential for optimal water resources management and planning. In this paper using the predictions obtained from the ECMWF climate model, monthly stream flow forecast was made in Shahroud river Subbasin, part of Sefidrood basin northwest of Iran. To achieve this aim, using monthly precipitation forecasts from ECMWF climate model in tandem with SVR data-driven modeling, as a rainfall-runoff model, the stream flow was predicted based on the predicted precipitations. First, the results of precipitation forecast, for the desired historical period, up to a 3-month forecast horizon for the study area were obtained from the Climate Data Store. Then, by using the SVR driven model, a linked Climate-Data-driven model was developed to predict the flow up to a 3-month forecast horizon. The results showed that flow forecasting based on climate forecasting models is more accurate for the forecast horizon of the next month than two and three months. So that the forecast horizon of the next month has the highest Nash-Sutcliffe coefficient, in calibration 0.77 and in validation 0.48. The highest correlation coefficient in calibration 0.87 and validation 0.69, the lowest root mean square error in calibration 6.8 and validation 6.3 million cubic meters and also has the best relative bias value for calibration 0.96 and validation 1.1. Also the results, based on the POD and FAR probabilistic indices, showed that the developed predictive model has a high ability to detect different states of stream flow events, especially for extreme flows event.

The reduced groundwater levels of plains have led to increased water extraction cost, increased energy consumption, reduced water quality, and the appearance of subsidence. Structural subsidence in plains could be directly resulted from reduced level of groundwater as well as destruction of alluvial texture in aquifers. Although it results from the compacted underlying layers of soil, subsidence represents unpleasant outcomes in the future. The compacted underlying layers and reduced water table in groundwater basins reduce the water storage space. In other words, reduced groundwater storage is a rational consequence of subsidence (Sharifikia et al., 2014). Scientific investigation and various instruments, including mathematical models, are required to overcome the problems of groundwater resources (Gaura et al., 2011; Hu et al., 2010). Mathematical models allow for investigating changes in the current and future situations of groundwater tables by incorporating different influential factors (Yaoutia et al., 2008; Zhang, 2010). Therefore, using mathematical models, the situation of an aquifer can be simulated by collecting information on the inputs and outputs of the groundwater system at a small cost in a short time. Such simulation models can provide the mutual effects of surface water and groundwater in short- and long-term periods. MODFLOW is among the most important mathematical models (Lachaal et al., 2012).In this current paper the approaches to minimize subsidence in Abhar plain is evaluated that based on an empirically derived relationship between cumulative subsidence rates and groundwater levels is used with common groundwater model software MODFLOW. Also, the vertical changes in the ground structure were nonlinearly modeled based on the cellular changes of the finite difference method. To determine the essential factor of the changes, the fuzzy model approach and spatial-statistical analysis were adopted. The largest effect on the appearance of subsidence was studied by the regression relationships of the corresponding points in the spreadsheet setting.

Abhar Plain is located in the northwest of the Namak Lake Basin. It occupies an area of 1926.5 km2, 1040.92 km2 of which is plain, while the remaining area is composed of mountains. The maximum and minimum elevations of Abhar Plain are 2166 and 749 m, respectively. The saturated area of the plain is approximately 657.9 km2 – the corrected area is 657.4 km2. The groundwater extraction area of the plain includes 1359 water wells with an annual discharge of 233.36 million m3, 169 fountains with an annual discharge of 4.88 million m3, and 101 aqueducts with an annual discharge of 1.3 million m3.Considering that parameters involved in the calculation of the final subsidence layer had reduction or enhancement effects on each other and given the descriptions on the overlapping functions, the current study employed the gamma operator. Computation was performed by the MODFLOW-2005 engine in GMS v.10. The study period was selected to be 119 months, which could be made more accurate and rebuilt based on the maximum available data. The computation engine of PCG2 with 100 outer and inner iterations, a critical convergence variation limit of 0.01 m, and a critical convergence error limit of 0.01 m3 per day was selected. 75% of the interval length was used for calibration in non-stable conditions. After seven executions of the calibration model with a certain number of internal iterations, the optimal final number of surface feeding, horizontal hydraulic conductivity and horizontal hydraulic conductivity anisotropy, specific yield in the form of pilot extraction points, and transferability parameters in the boundaries, and the waterway network in the form of the bunches of lines. The layers were also used to develop the fuzzy subsidence model.According to the described fuzzy theory, each of the basic parameters influencing or representing subsidence was transformed into a standard raster map in the range of 0-1 by a linear equation. The fuzzified layers included water level reduction, the difference between the initial and final water levels, horizontal hydraulic conductivity anisotropy, horizontal hydraulic conductivity, saturated aquifer depth, surface feeding, extraction flow rate, and specific yield. Direct fuzzification was applied to all the parameters, except for surface feeding, to which inverse fuzzification was applied. Table 2 shows the zoning errors of the selected layers. The parameters of Table 2 were used to develop the fuzzy model. The coefficients of the aquifer were merely extracted from the last calibration round of the groundwater flow model.In the next step, to investigate the spatial variations of subsidence occurrence, the land-use layer was utilized as the statistical analysis basis of the subsidence raster output.

The results of the current study indicated that subsidence modeling can be carried out by linear regression analysis with a determination correlation coefficient of 70%. Among the influential parameters, merely the spotted aquifer depth layer had a correlation of 30%. The fuzzified variations of the layers with the enhancement-reduction gamma combination had the highest correlations with the spotted subsidence layer of the MODFLOW output. However, the correlation was noticeably lower than expected. In the subsidence model, the final raster layer was transferred to the GIS setting using dispersed points. After zoning using the regional analysis command in two manners, the separate indicators of land-uses and a basic land-use set were extracted. These results revealed that the urban land-use accounted for 26% of subsidence, even though it occupied merely 4% of the aquifer’s area. The agricultural land-use (including gardens), which accounted for 42% of the aquifer’s area, involved 56% of subsidence events. Finally, occupying 54% of the aquifer’s area, the idle land-use accounted for merely 19% of the vertical changes of the aquifer’s structure. The high subsidence in urban areas was noticeable. Considering the alluvial groundwater yield, the effect of water level reduction can be seen with small spatial distances.

Precipitation forecasting is one of the most important tools in water resources planning and management. Recently, new methods called atmospheric dynamic models have been used to predict many hydro-climate variables including precipitation. Before using the predictions of these models in planning and decision making, the accuracy of the mentioned predictions and their bias correction should be evaluated. Therefore the objective of this study is to ascertain the biases and to combine the results of precipitation forecasting with a set of global dynamic forecasting models. To achieve this aim, firstly the precipitation forecast results of each model were compared separately with the regional recorded precipitation data in the period of 1982 to 2017. Using this approach, the systematic errors were removed and corrected, i.e. using the quantile mapping method. This work was done for different forecast periods and also for different months. Furthermore, based on the accuracy of each model, a hybrid/multi-model prediction system was developed using Bayesian averaging method (BMA). The results showed that after the bias correction, using the quantitative mapping, at least one model among 78 prediction models have a relatively high correlation value of about 0.7. This result was recorded for the next one-month horizon. This correlation was increased to more than 0.8, by combining 78 predictive members, using Bayesian averaging method. Therefore, the accuracy of the predicted precipitation increases significantly using bias correction in tandem with combining the prediction models.

Accurate estimation of pollution decay coefficient is due to its effect on the oxygen consumption of the river’s solution of important issue, in managing the quality of water resources. The main purpose of this paper is to determine the decay factor of two rivers of Iran, named Talar and Babolrood Rivers. It has been done by measuring the field and elucidating the common empirical equations in two seasons of spring and summer. The qualitative parameters including DO, BOD, pH, EC, nitrate, phosphate and temperature in Talar River were measured in 5 sections of a total length of 4 km long, and in Babolrood River in 3 sections at a total length of 2.8 km, in May and August 2018. Then the decomposition coefficient was estimated using Streeter-Phelps Analytic Model. DO and BOD values were also analyzed in both spring and summer seasons. Based on the results in both case studies, the decay rate in summer was more than spring. The decay rate in Talar River was calculated equal to 1.57 in spring and 6.11 in summer, and in Babolrood, spring was 1.2 and in the summer was 8.34 (1/ day). Also, based on the slope changes, the dissolved oxygen content in both rivers increased in spring and decreased in the summer. In Talar River the BOD was equal to 4.34, in spring and 7.44 mg per liter in summer, and in Babolrood River in spring was equal to 2.5 and in the summer equal to 3.2 mg per liter.

Optimal water consumption and the supply & demand management would require a better focus on the economy of water resources. This study evaluates the effects of virtual water on the economic value of water resources using a mathematical model. The model has been applied to the Arak Plain, central Iran, for four strategic crops of wheat, barley, alfalfa and corn with three weighting methods of “cultivated area”, “water consumption volume”, and “income”. Meteorological and agricultural data, water consumption data as well as incomes and expenses of the water section in 2015-2016 were used in this research. Results show that the economic value of agricultural water resources in the Arak Plain increased for all the three weighting methods by considering the virtual water that led to substituting the products. Replacing alfalfa with sorghum would increase the economic value of water resources by 36.0%, 28.0%, and 40.5%, using the three weighting methods of “cultivated area”, “water consumption volume”, and “income”, respectively. Increasing the economic value of water resources is one of the main ways towards sustainable development and food security.