abolhasan fathabadi

The Objective of this research is to provide a model to calculate flood discharge values with a certain return period by using the geometric features of the watershed and by providing multivariable models.To understand flood occurrence in the Gorganrood watershed, we studied 11 hydrometric stations in Golestan province. Various influential parameters, including physiographic factors, vegetation, land use, soil science, geology, and weather including 31 independent variables were selected in this research. Data analysis and flood modeling were done by the multivariate regression statistical method. In this way, flood regional models using the Enter method have higher accuracy and precision than the models obtained using the Forward method. In general, 13 variables out of 31 independent variables included in the multivariate regression test have been identified as the most important and influential factors in the occurrence of floods in the Gorganrood watershed. The effective variables include: Gravel's coefficient, percentage of the area of permeable formations, watershed slope, concentration time, watershed area, flow velocity, percentage of land area with a north direction, percentage of land area with a south direction, percentage of pasture land area, percentage of agricultural land area, percentage Waterway density is ranked 3, as are average annual temperature and maximum annual rainfall.

By using these relationships (models), it is possible to obtain the values of the maximum instantaneous discharge with a return period of 2, 10, 5, 25, 50 and 100 years in the basins without statistics under the sub-basins of Gorganrood.

Water and soil play an important role in all human environmental activities and controling physical, chemical and biological processes in the environment. The existence of limitations of water resources, especially the fresh water of rivers and dams, makes it necessary to pay more attention to them against pollution. Contamination of water and soil with heavy metals are serious environmental pollutants that enter the environment in many different ways with natural and human origin and cause serious damage. Karaj Dam is one of the most vital sources of drinking water supply in the metropolis of Tehran, which faces a serious risk of pollution due to the development of human activities. This study investigated the concentrations of heavy metals (Zn, V, Ti, Pb, Ni, Mn, Cu, Cr, and As) in 40 soil samples and sediments, as well as five suspended sediment samples, in the river of the Laniz watershed in Karaj, Iran. Due to the construction activities in the watershed including the construction of the Tehran-North Freeway, the natural condition has been affected over the past decade and therefore need to use and new inventory method to separate the effects of natural geochemical from humane effects. The single-element indices of contamination factor (CF), geoaccumulation index (Igeo), normalized enrichment factor (EF), and ecological risk potential factor (ER), as well as two multi-element indices of pollution load index (PLI) and ecological risk index (RI), were employed. At first the elements and samples were grouped by principal component analysis (PCA) and cluster analysis. FA distinguished two groups of elements; called natural and human-natural origins. The cluster analysis divided 50% of the freeway-affected samples into two distinct clusters. Based on the CF index, the As contamination was extreme in the suspended sediment samples and soil samples related to the Freeway while the Pb and Mn carried out the next rank. Also, the single Igeo index revealed the high As contamination. Moreover, based on the Igeo, most suspended sediment samples related to the Freeway put in the extreme contamination class for all of the elements. However, the Mn-modified enrichment factor showed only one freeway-affected and two freeway-unaffected samples exhibited extreme enrichment, and the enrichment of other samples was low or moderate. Unlike the single-element indices, PLI and RI showed that the suspended sediment and soil samples related to the freeway had low pollution load and ecological risk. Compare of single and multiple contamination indices revealed an inconsistency of pollution risk that can be addressed into the background value (in single indices) and the toxicity response factor of the metals (in multiple indices). Results indicated that due to spatial-temporal changes of human effects, it would be better to use different values for both background and elements toxicity response. Therefore, to reach a reality image of contamination risk, more researches are needed to elucidate the mentioned standard value based on the regional variations.

Over the past decade, the trend of declining water levels as well as declining groundwater quality along with quantity is a major issue in water resources management. In this study frequency ratio, statistical index, weight of evidence, classification and regression tree (CART) algorithms and random forest methods were used for groundwater salinity hazard mapping in the southern part of Bakhtegan watershed. After considering the salinity threshold for groundwater (EC<1000 µSiemens/cm), As groundwater salinity map, thematic layers of 21 groundwater salinity conditioning factors including altitude, distance to anticlines, distance to synclines, distance to salt plans, distance to saltwater lakes, distance to dams. Soil salinity index, topographic wetness index, curvature, plan curvature, plan curvature, flow accumulation, flow direction, slope, aspect, land use, soil type, climate, land cover, groundwater drop, groundwater level were prepared. EC data were divided into two categories of training and validation and by comparing the salinity map of groundwater with 21 independent factors; the weighting of two-variable methods and the parameters of multivariate methods were estimated. According to the selected factors in the southern plains of Bakhtegan watershed, the results of this study stated that altitude factors, distance to salt plans, distance to synclines and anticlines and distance to saltwater lakes are more important in the occurrence of groundwater salinity in this region. The results of validation of bivariate models estimated the amount of area under the curve (ROC) for frequency ratio methods (0.923), statistical index (0.905) and weight of evidence (0.908), which indicates better performance of frequency ratio method compared to two other methods. Also, the results of multivariate methods showed better performance of random forest method with matching coefficient values (0.91) and correlation coefficient (0.85) than CART with matching coefficient (0.89) and correlation coefficient (0.82). Finaly, in any research, the efficiency of the models depends on the appropriate selection of the effective factor in the occurrence of the phenomenon under study, the quality of the collected data and the quality of the maps used.

The scour depth estimation is of great importance. Researchers have provided many empirical relationships based on laboratory and field work, but so far no relationship has been found to be satisfactory in different situations. In this study, the accuracy of different experimental (individual) relationships was evaluated in two stages before and after the correctional correction. The results show that before and after the bias correction, the individual relations of the National Institute and Mason with the mean square error of 0.87 and 0.23 meters are the most accurate. The combination of individual relationships with combined methods models shows that the GRA and EWA methods with a mean square error of 0.25 and 0.23 meters were the most accurate among the direct methods in before and after bias correction stage. The error of the AICA and BICA indirect methods in before and after bias correction is similar to the best single relation and could not improve the results of the individual relationships. The results of local method (KNN) and artificial intelligence (LS-SVM) method before and after bias correction are equal and estimate scour depth more accurately than individual relations. Comparison of different combinational methods in before and after bias correction shows that individual relationships with maximum scour depth using different combinational methods can improve predictive accuracy.

In any water resource management plan, there is a pivotal need to undertake the future conditions to allocate the water resources to different sectors (e.g. drinking-water supply, agriculture sector, etc.) more efficiently. Meanwhile, it is important to forecast water resources inflow for future months. To this aim, it is of prime interest to adopt models that are capable of coping with data scarcity problem and able to forecast the stream flow with the least possible error. The current study was aimed at forecasting the monthly inflow of the Boustan Dam by employing three models namely: time series method, Artificial Neural Network (ANN), Support Vector Machine (SVM), and their ensembles. The hydrometric data was obtained from the Tamar Station. Afterward, the models were compared by using several evaluation criteria. According to the Akaike and Schwarz criteria, the ARIMA (2, 0, 0) (1, 0, 1) was found to be the best time series model with a parsimonious behavior. Moreover, the ANN model with two and four input neurons and the SVM model with three input neurons were the best performing models compared to their other counterparts with different input numbers. Considering the evaluation criteria altogether, the time series method was the best performing model with the RMSE, AARE, MBE, and CE values of 0.88, 4.71, -0.024, and 0.36, respectively. Therefore, the time series method was introduced as the premier model for monthly inflow forecasting in the studied stations.

In order to develop management plans for water and soil conservation, it is necessary to determine the sources of sediment production in watersheds. During the past three decades fingerprinting technique has been used extensively in determining the contribution of different sources of sediment. In this study, was carried out sediments fingerprinting and determine the contribution of each source to sediment production of the Toulbane watershed in Golestan province. To this end, 44 source samples were collected from forest, pasture, agriculture and bank erosion. Also 8 sediment samples were collected using Philips time-integrated sediment sampler. Afterward, the concentration of 34 geochemical properties was examined in the laboratory using the ICP device. Next, the optimal composite tracers were determined to discriminate sediment sources by using statistical tests including mass conservation test and Kruskal-Wallis. The contribution of different sources to sediment production was determined using the multivariate mixing model. Finally, the uncertainty in the case of a low number of data, was examined using the Monte Carlo method. As a result, after statistical tests, 12 tracers were selected as the optimal composite fingerprints. The bank erosion was main source to sediment production with 52.18% and the forest had the lowest contribution to sediment production with 4.39%. The contribution of agriculture and pasture was 33.23% and 10.21%, respectively. According to the uncertainty analysis, bank erosion is the most significant source to sediment production. Also, the high difference between the upper and the lower boundaries in different sources indicates high uncertainty.

In order to develop management plans for water and soil conservation and erosion control, it is necessary to identify the sources of sediment yield and the contribution of each source in watersheds. The aim of this study was to determine the contribution of different geological units to sediment yield of the Toulbane watershed in Golestan province. In view of this, samples were collected from different geological units. Collecting suspended sediment samples was carried out using Philips time-integrated sediment sampler over a one-year period (2017). Afterward, the concentration of 34 geochemical properties was examined for 41 source samples and 8 suspended sediment samples in the laboratory using the ICP device. Next, the optimal composite fingerprints were determined to discriminate sediment sources by using three statistical tests including mass conservation test, Kruskal-Wallis and stepwise discriminant function analysis. As a result, 15 tracers were selected as the optimal composite fingerprints and the contribution of different geological units to sediment yield was determined using the data derived from these tracers and implementing the multivariate mixing model. According to the results, the Qal and Jsl s.sh units had the highest contribution to sediment yield with the respective values of 68.22% and 28.75%. The Qal contains quaternary deposits and the Jsl s.sh (Shemshak formation) consists of sandstone and shale materials; both of which are highly sensitive to erosion. Hence it is necessary to carry out erosion control actions on erodible units to reduce sediment entrance to the river.

Given the importance of climate change issue, consideration of the climate and hydrological data trend has become very important. This study aimed to evaluate the effectiveness of different methods to remove the effect of autocorrelation in assessing the trend of parameters such as temperature, precipitation and discharge of the Eastern Gorganrood Basin, Golestan Province. For this purpose, in addition to conventional Mann-Kendall test, nonparametric tests like MK-PW, MK-TFPW, MK-VCA, MK-SA and MK-HSA were used for trend analysis. As well as determining the slope of the trend line and identifying the jump points, San slope estimator and Mann-Kendall sequence (SQMK) were used. The results showed that the values of temperature and precipitation at all stations uptrend and this was significant for temperature in Lazoreh stations and Ramian. Also, discharge flow in Arazkuseh station had significant descending trend. Using nonparametric tests decreased Z statistic. In most stations, MK-PW method had the minimum significance and MK-TFPW and MK-VCA methods showed better performance and reduced the risk of Type I error. In the station where there was no significance in the first and higher order autocorrelation, MK-VCA and MK-PW methods provided similar results. MK-TFPW showed a different behavior due to calculating the autocorrelation after the removal process. Considering of turning points in Mann-Kendall sequence test indicated that at Arazkuseh, Ramiyan, Nodeh, Tamar and Ghazaghli stations, the begining point of trend was related to the years 2000, 1987, 1988, 2001 and 2001 and only in 2 stations including Tamar and Ghazaghli, it was significant in the years 2006 and 2005 (at 5%). In all of the stations, temperature had an abrupt change point of different trend and the time of increasing temperature was equal to the point jump of discharge decreasing trend in 1993 in Arazkuseh station.

In water resources management, there is a critical need to the prediction of the amount of inflow into the water supply system in order to be aware of future conditions and planning for optimal allocation of water resources to different sectors such as drinking, agriculture and. The aim of this study is to forecasting the monthly inflow to the Gorgan dam for future. To this aim, the data of the Qazaghli station with a 47-years history period and three Time series, neural network and Support vector machine models used for prediction. According to the obtained results, the ARIMA (1, 0, 0) (1, 0, 1) was found to be the premier parsimonious time series model based on the Akaike and Schwarz criteria. Moreover, The ANN model with 2 input and 10 neurons tuning and the SVM model with one input were the best performing models. Finally, according to the obtained results and evaluation criteria, the SVM model has the best efficacy in comparison with two other methods. The RMSE and AARE was 5.31 and 1.07 for SVM model, respectively; 9.88 and 2.78 for neural network, respectively and 8.84 and 1.07 has been obtained for Time Series model, respectively. Based on the results of this research, the best model to predict the monthly discharge input to the Gorgan dam was SVM.

Despite modern scientific knowledge and computational power in hydrology, the key to properly addressing hydrologic uncertainty remains a critical and challenging one. Here, we applied lumped HBV hydrological model to describe the uncertainty in runoff prediction in Chehl-Chay watershed in Golestan province. We applied a new framework for uncertainty analysis that is rooted on ideas from predicting model residual uncertainty. The uncertainty calculated by local Errors and Clustering (EEC) is compared with estimates from two non parametric methods (quantile regression (QR) and random forest (RF)) and a parametric method (GLUE). Firstly, the model parameters were optimized by Shuffled Complex Evolution approach and model residuals of test data were computed. Fuzzy clustering in EEC is carried out by the fuzzy c-means method and employs four clusters, predictive discharges, observed discharges, rainfall values and residuals in study basin. The results of this case study show that the uncertainty estimates obtained by EES which is trained by SVM gives wider uncertainty band and RF gives narrower uncertainty band. The best overall uncertainty estimates according to the PICP, MPI and ARIL indices were obtained with QR and then EEC. In comparison with non-parametric, with respct to all indices nonparametric methods had better performance than GLUE method.

The aim of study was set to evaluate components of the hydrological cycle in Tilabad Watershed during future periods as affected by the predicted land use change. For this purpose, using MSS (1986), ETM (2000) and OLI (2015) Landsat satellite images a land use map was produced in seven categories. Then, two likely future land use maps were predicted based on Markov chain modeling and cellular Automata approach for the years 2025 and 2040. Simulation for 2000-2014 years and by using SWAT model and SUFI2 program was done. The results of maps assessment of the years 1986, 2000 and 2015 with the kappa index 0.9553, 0.8655 and 0.9612 respectively, shows destruction trend overall. The match assessment between simulated and actual land use for 2015 showed a kappa coefficient 0.7993. In the analysis of land use was determined with degradation of the region to runoff, sediment, the water, subcortical flow, and evapotranspiration increased and the amount of groundwater flow, backflow from the aquifer surface, deep aquifer recharge, and permeability and Hydraulic conductivity were reduced significantly. Also, the value of peak and medium was increased and base flow as reduced. Generally, the effects of changes in 2040 Compared to 2025 was more.

In all stations the least amount of P was related to seasonal Mann-Kendall method that series data has autocorrelation. The stations have first-order correlation MK-PW method have highest amount of P. TFPW method has shown different behavior because of calculating the autocorrelation after removing trend. Comparisons of methods showed MK-VC, MK-BB and HERISH methods better than others and able to reduce the probability of first error. To further trend study and relationship between climate and hydrological parameters, rainfall and river flow in Nodehkhandooz station was studied using MASH approach in different months. The results showed that river flow has decreasing trend in the months when withdrawal of river is high. Most of climatic and hydrological data has autocorrelation, which makes Kendall method detect trend the series data without trend. In this study the effect of different removal aoutocorrelation methods were studied in Kendall trend detection of rainfall and runoff in 4 hydrometric stations in Golestan province. The methods used to remove the autocorrelation was MK, MK-PW, TFPW, MK-VC, MK-BB, SEAS and HERISH. The results showed that rainfall in Nodehkhormaloo station and river flow in Ghazaghly and Gholitapeh stations have significant increasing and decreasing trend respectively.

Streamflow recession indicates the river network balance between revenue and losses of river. Recession curve expreses the storage- output relationship for the catchment. The aim of this study was trend anaylysis, modeling of Streamflow recession and uncertainty estimation in Shahmokhtar station on the Bashar River in Kohgiluyeh and Boyer Ahmad province. Based on the results of the Mann-Kendall, discharge trend at studied station was very little increasing, but there was no significant trend. After determination of parts, the Maillet, Baronz, Boussinesq, Horton, Drouge and exponential reservoir models were fitted. In this regard, initially the different parts of the recession lamb (in multireservoir models) were determined and the parameters of Maillet, Barnes, Boussinesq, Horton, Coutagne, Drogue and exponential reservoir models were estimated. To calibrate the coefficients of models, in addition to overlaying the estimated and observed hydrographs, the sum of square error criteria was used. Comparing the results also showed that models of Drouge, Barnes, Horton, Boussinesq exponential reservoir and Maillet could be fitted well, respectively.

When a stream is partially obstructed by a bridge pier, the flow pattern around the pier is significantly changed. Changes in flow pattern are the cause scour around piers. Bridge pier scouring estimates, is an important parameter in the design of bridges because inattention to it may cause damage or reduce the life of the bridge [1-4]. The safe and economical design of bridge piers requires accurate prediction of the maximum scour depth around their foundations [5].
Mathematical Principles of SVM is based Russian mathematician researches [6]. Ghazanfari Hashemi and Etemad Shahidi had predicted bridge pier scour using SVM method in a laboratory model. They showed that this method is more accurate than empirical relationship. Although extensive studies have been conducted on the pier scour, but a valid relationship does not exist that gives satisfactory results in different conditions [5].
Bates and Granger is one of the first research works in the field of hybrid approach [7]. Shamseldin et al. was used combination methods such as simple average, weighted average and neural network to predict floods [8].
Studying literature reveals that there is a lack of reliable formulas for prediction of the scour depth to cover different condition. The aim of this study is combination of the various pier scour relationships to predict the scour depth using conventional and intelligent (SVM) methods and combination of effective parameters of this phenomenon.

The calibration of hydrologic models is a worldwide challenge due to the uncertainty involved in the large number of parameters and the inability to reliably measure the distributed physical characteristics of a catchment results in significant uncertainty in the parameterization of physically based, semi-distributed models. The difficulty even increases in a region with high seasonal variation of precipitation. Therefore, a successful application of a hydrologic model in applied water research strongly depends on calibration and uncertainty analysis of model output (). Shen and et al. () quantify the parameter uncertainty of the stream flow and sediment simulation by SWAT model in the part of China. The research indicated that only a few parameters affected the final simulation output significantly. Shape and et al. () assessed the capability of the Soil and Water Assessment Tool (SWAT) model to capture event-based and long-term monsoonal rainfall–runoff processes in complex mountainous terrain and found that high elevation steep sloping regions were generally base flow dominated while lower elevation locations were predominately influenced by surface runoff. In this paper, the Generalized Likelihood Uncertainty Estimation (GLUE) method was combined with the Soil and Water Assessment Tool (SWAT) to quantify the monthly stream flow in the eastern Gorganrood river basin.
The Golestan Regional Water Company (GRWC) monitoring site at Gazaghly was chosen as the outlet for the entire watershed since it is the lowest monitoring station on the river not subject to dam influence. Annual precipitation decreases in the west to east direction, low (200 mm) to high (880 mm) and from south to north direction. Model has been calibrated and validated using monthly runoff flow data of ten years 1984 and 1993. Data pertaining to year 1984-1990 has been used for calibration and 1991-1993 for validation.
In semi-distributed models such as SWAT, it is necessary to identify the most sensitive parameters to obtain a better understanding of the overall hydrologic processes before calibration. Based on this study, only a few parameters affected the final simulation output significantly. The parameters such as CN2 (curve number), GWQMN (threshold in the shallow aquifer), RCHRG_ DP (deep aquifer percolation fraction), ALPHA_ BNK (base flow alpha factor for bank storage), ESCO (soil evaporation compensation factor) and SOL_ K were found to be the most sensitive parameters. According to results, the parameter CN2, was the most effective parameter on the output discharge of the studied area and CN2, was identified as a main source of uncertainty in results. Statistical model performance measures, coefficient of determination (R2) of 0.80, the Nash-Sutcliffe simulation efficiency (ENS) of 0.72, for calibration and 0.83, and 0.80, respectively for validation, indicated good performance for runoff estimating on monthly time step in the outlets of the Gazaghly gauging station. 69–74% of the observed runoff data fall inside the 95% simulation confidence intervals in the calibration and validation periods. The evaluation statistics for the daily runoff simulation showed that the model results were acceptable, but the model underestimated the runoff for high-flow events.
SWAT was applied to simulate monthly runoff in part of the Gorganrood river basin. Results of uncertainty analysis indicated that SWAT model had large uncertainties for calibration period, although the simulation of monthly runoff for the Gazaghly station was satisfactory during the calibration period and in the model calibration stage 69 of runoff observations were within the corresponding 95% confidence interval. This study would provide useful information for hydrology modeling related to policy development in the Gorganrood river basin and other similar areas.

Information on water quality and pollution sources is important for the implementation of sustainable water resource management strategies. In this research¡ to evaluate spatial variation and the interpretation of large complex water quality data taken at seven different sites along the Gorganrood River were subjected to multivariate statistical analysis. The factor analysis generated two significant factors¡ which explained 97.80% of the variance in data sets. Factor 1 and factor 2 explained 86.27 and 15.76% of the total variance in water quality data sets. Principle components analysis results revealed that surface water quality was mainly controlled by Ca and pH parameters. In next step¡ information entropy theory applied to interpret the stability of surface water quality variation in each factors and each parameters. Overall results revealed instability in data recorded in Tamar and Galikesh sites and in factor 2¡ EC and TDS¡ Lazoreh has highest instability. With a well knowing the factor score and overlaying entropy¡ infactor1¡ Mg and Cl had highest stability. The ranks of information entropy values for Mg parameter at Galikesh¡ Tagi Abad¡ Basir Abad¡ Ramian and Tangrah sites vary noticeably due to geological formation. In general¡ sampling uncertainties are highly site specific.

Groundwater resources are the most important sources of water consumption (agriculture، industry and drinking water) in arid and semi-arid regions. The management of these water resources is more expensive and difficult than surface waters; therefore more complicated and economic methods are needed for determination of their quality and quantity. In this study geostatistical methods of kringing and cokriging and multilayer perceptron (MLP) artificial neural network model were used to estimate the quality parameters of SO42-، TDS، Ca and TH. The methods were compared in order to understand which one is the best method for this estimation. Data obtained from 50 wells located in Koohpayeh plain in Isfahan province was used for this study. For estimation of the functionality of these methods in simulation of the parameters، RMSE and correlation coefficient were used. The results showed that for all of the studied parameters، MLP with a lower RMSE and higher correlation coefficient showed the highest precision followed by cokriging. Kriging showed to have the lowest precision in stimulation of the quality parameters.