جستجوی مقالات مرتبط با کلیدواژه "منحنی سنجه رسوب" در نشریات گروه "جغرافیا"

This study was aimed at identification and determination the spatial variations of sediment yield in Qarasu watershed located in Ardebil province, NW Iran, considering the negative consequences of erosion and sediment yield. In this regard, the monthly time scale, as the basis of work, was used to prepare the sediment rating curves. Preparation of monthly sediment rating curves based on sample flow and corresponding sediment discharge data in 19 hydrometry stations at the watershed during 14 years (2002-2015). All of statistical analyses were done by SPSS statistical software. Also, presentation of spatial variations in sediment yield over the watershed were made possible through the capability of GIS. The results of regression relations between flow and sediment discharge showed a close and significant relationship on a monthly time scale. The regional generalization of these relations for the whole basin showed that the seasonal difference was evident and the highest and lowest coefficient of determination was allocated to May (R2 = 0.74) and August (R2 = 0.55), respectively. Also, the highest and lowest sediment transport was observed in spring and summer, respectively, indicating the type of rainfall-runoff regime dominated on Qarasu watershed. In terms of the annual sediment yield, Yamchi station with 3970 tons per year and Nanakaran station with 66 tons per year, have the highest and lowest sediment yield among 19 stations of Qarasu watershed, respectively. According to the high levels of sediment yield in the Balikhly and Khyav sub-watersheds, it was recommended that more attention be paid to protecting and strengthening the water and soil elements and reducing the driven forces of erosion and sediment production in these sub-watersheds.

Sedimentation is one of the most important issues in the watershed. Due to the problems caused by sediment, it is necessary to investigate the relationship between hydrogeomorphic variables affecting sediment production and suspended sediment load in the watershed. The purpose of this study is to model the relationship between suspended sediment loads with hydrogeomorphic variables of the basin and to extract geomorphic features and their relationship with sedimentation in the watershed. By simple random method, two cows and conifers, which include 8 specific sub-basins and are equipped with hydrometric stations, were selected. Statistical multivariate regression method was used to analyze the relationship between geomorphic variables with sedimentation of each sub-basin. The results of the study of the relationship between geomorphic characteristics and sedimentation of sub-basins showed that the amount of sediment produced was positively correlated with slope index, roundness, drainage texture, rainfall, unevenness and basin area and was significant at the level of 0.001. In order to influence the variables on the sedimentation rate of the sub-basins, the principal component analysis and cluster analysis methods were used. The results showed that the three factors of roundness coefficient, slope coefficient and drainage texture coefficient of the basin explain 44.62, 25.22 and 16.74% of the variance of all research variables, respectively. In total, the three final extracted factors were able to explain 87% of the variance of all research variables.

At present, it is important to accurately predict the hydraulic parameters of flow and sediment for better operation and management of rivers.Today, quasi-two-dimensional mathematical models have been widely used as an optimal and efficient solution in the hydraulics of river flow and sedimentation Zahiri et al. (2018). Mathematical models are one of the most important and accurate tools for predicting the amount of sedimentation in riverbeds and dam reservoirs, which are based on the equations governing the phenomena affecting the transfer, sediment accumulation. Gholami,et al. (2017).  Raeisi et al. (2019), in examining the temporal phenomena of the sediment measurement curve and comparing it with several statistical methods to estimate the suspended sediment load of Gamasiab watershed, showed that the time series model of the transfer function compared to other models used has higher performance.  Lai et al. (2019) in a study of the capacity of current and sediment transfer with 3D model for open surface channels showed that the model of good simulation between flow and sediment for forecasting is presented and is well matched with experimental data.In the present study, an attempt has been made to examine and determine the most appropriate models for estimating the suspended sediment load of the watershed of Hamedan River by using different models.

Abshineh basin is located in the southeast of Hamedan city and its river regime is under the semi-humid cold mountainous snow-rainy and permanent climate.In this study, the amount of sediment yield of Hamedan Abashineh dam watershed using USBR models, the middle curve of the categories Seasonal measurement curve and FAO method were estimated and while direct sampling of suspended load, the selected model by   logarithmic conversion error correction method (CF1, CF2) and GS + model were statistically analyzed and evaluated.

Examination of CF1 and CF2 correction coefficients shows that the FAO method has evaluated the amount of suspended sediment over time better than the two linear USBR methods and the intermediate method with the least amount of error.In the model without data segmentation, the FAO method with the lowest relative error percentage and the mean power of the second error was selected as the optimal method, and the similar hydrological period method was selected as the most inappropriate method for estimating suspended sediment in the basin. The results show that the USBR method follows the normal distribution to some extent and the FAO method follows the perfectly normal distribution.The results of data analysis in Kriging method show that the regression line fitted in USBR method is not well adapted and could not provide a complete analysis of sediment observation data. However, in the FAO method, it is observed that the computational data have a high and good agreement with the fitted regression line. The results showed that FAO method, due to considering more parameters in boundary conditions and the lowest amount of correction coefficients of CF1 and CF2, estimates the amount of suspended sediment with the least amount of error compared to other methods over time with more acceptable accuracy and efficiency.Also, the results of the Gs + model show that the FAO method calculates suspended sediments more accurately in terms of tons per day and has more fit and consistency with the observed sediment values, and the USBR method has the least fit.

The output results of Gs + model and comparison of suspended load estimation in models show that FAO method due to more compatibility and accuracy, more accurate sedimentary fit with observational data and with least error, as the best model and USBR method with matching and fitting Less with observational data in the next degree and similar hydrological period with the highest relative error percentage of 100.34 and low correlation coefficient were selected as the most inappropriate method for estimating suspended sediment in the basin.Reviews show that hydrological models had different results than each other. So that in the model without data segmentation, FAO method compared to other methods with the lowest percentage of relative error and the average power of the second error as the optimal and appropriate method of more accuracy and efficiency for estimating suspended sediment in a modified form in the Abshineh basin Hamedan.

Understanding the status of discharge and sediment of watersheds area provides an understanding of the hydrological processes. Uniform assuming of discharge and sediment in a watershed leads to the application of unit planning and management for the entire watershed and leads to a failure to achieve the goals. The present study was conducted with the aim of studying spatial variations and zoning of discharge and sediment values in Ardabil province during 10 years (2005-2014) at 37 stations with hydrometry in Ardabil province. IDW interpolation methods, simple kriging, ordinary kriging and cokrigring were investigated to determine the best method for studying spatial variaions, discharge and sediment variables. Based on the results of the evaluation indices of IDW (RMSE=1.172, MAE=0.644, MBE=-0.2, MSE=1.373) and cokriging (RMSE=0.107, MAE=0.08, MBE=-0.001, MSE=0.011) and the correlation coefficient between measured and estimated values by IDW (r=0.429) and cokriging (r=0.554) respectively, as the best method for evaluating the discharge and sediment variables were chosen. Based on the results of the IDW method, the amount of digestion in the Western parts of Ardabil province is greater (4.84-5.8 m3/s), and in the central section, this amount is the lowest (0.04-1 m3/s). According to the results of the interpolation of the sediment variable with the cokrigring method in the Western and Southern parts of Ardabil province with a higher amount of sediment (0.16-0.19 ton/ha/year) and in the central parts of the sediment deposition (0.01-0.04 ton/ha/year).

Estimation of the rivers sediment load has high complexity due to effecting different parameters in this aspect. Regarding the power relationship between discharge data and suspended sediment load use of sediment rating curves is one of the most common methods for determining the sediment yield in ungauged watersheds. Sediment condition shows the upstream characters and using of the obtained data makes a relation between erosion and sediment load. The different parameters such as climate, land use, data accuracy and the applied methods have the effect on the sediment rating curve shape. Agriculture activities such as tillage in the direction of slope lead to accelerated erosion in the watersheds, especially in the Mediterranean area. These decades many studies assessing the effects of climate changes in the future period and it affects on runoff. In this study, the main objective is to obtain sediment changes during the future decade (2011-2030) using the curve rating in sediment estimating. For this purpose, the IHACRES hydrologic model and the LARS_WG climate model were used.

The IHACRES model for seven hydrometric stations was calibrated and validated. This model is a rainfall and runoff erosion that require a little data for running including minimum and maximum temperature, rainfall, discharge and study are. This model defined as a lumped model and highly common in watersheds with scarce data. With running this model in all of the models the model parameters were calibrated. Also, the LARS_WG model was used for determining the weather changes that are occurring in the Samian watershed. This watershed has near to 4 thousand square kilometers that have many sub-watersheds. In this study, the watersheds in the west of the Samian watershed were selected for modeling. The average of rainfall in this area is between 220 and 457 mm, and the weather temperature changes in this region are high and that is between -32 to 34 C°. The results of LARS_WG showed the weather changes in each part of the hydrological model inputs that these changes were applied to the IHACRES model and the discharge flow rate was estimated for the future. On the other hand, using the observed discharge and sediment yield were calculated the sediment curve rate. By changes in flow discharge at the study stations, were calculated the suspended sediment discharges for the future period.

The results of the LARS_WG model showed that the amount of precipitation decreased to 3.68 percent and the minimum and the maximum temperature increased by 16.48 and 5.39 percent, respectively. Decreasing of the input precipitation in most part of the world particularly in Iran watersheds mentioned in many studies. One of the other the most important effect of the climate change in this area is minimum and maximum temperature increase that leads to evapotranspiration increasing and soil moisture loss. The results of the IHACRES model showed that this model has the suitable capability for simulation runoff in the study area, therefore, it was used for estimating the future runoff regarding climate changes. The model output showed that during the next decades the average flow rate in the hydrometric stations will decrease by a total of 16 percent and the number of peak flood events will increase, that the highest increase between the study watersheds observed in the Yamchi hydrometric station with a mean of 2.09 m3s-1 and 16 peak events with over 6 m3s-1. Using the obtained results of the climatic model, hydrological model and the sediment rating curve the suspend sediment changes were estimated for the future period. The result shows that these climatic changes will lead to a 47 percent reduction in the average of suspended sediment load at study stations.

The consequences of the climate changes have the significant effect on water resources quality and quantity. The aims of this study were calculating the weather changes and it's ruling on discharge and sediment yield changed. the results of this study indicate the effect of climate change on the Ardabil province watersheds is remarkable. Considering the environmental impacts of climate change and dependence on human life on the environment it is necessary to implement an appropriate approach for decent management in Watersheds.

Understanding and determining the amount of carried sediment by river have been considered by experts because of the importance and the role of sediment transfer in water-shed basins subject , among them is rivers organizing that carry out for erosion and sedimentation control or river-bed stabilization and flood eject. The current study aims at determining the efficiency of decision tree model in estimating suspended sediment of Meimeh river. The data used in the study includes sediment ,water , rain fall , and daily discharges related to the statistic periods from 1967-68 to 2009-2010. After data processing 554 records with accessible statistic discharges and their corresponding sediment were chosen and the obtained results were compared using sediment rating curve. To this aim, statistical criteria of R2 , RMSE, MAT , R and Bias were applied. To investigate, the rain fall effects and daily discharges on precise measuring of sediment by tree model, the data related to the rain fall and daily discharges were added to the model. The results showed that the decision tree model has presented reasonable results for the simulation of the suspended load in the present study station. So that based on criteria RMSE, MAE, r2 and Bias decision tree with less error than the sediment rating curve deposits is estimated. The results did not change much and sediment discharge rate is most correlated with the corresponding discharge of sediment.

This study was done for evaluating and providing an suitable model to estimate sediment load in the Almas Bridge water gage established on Balikhly river regarding harmful economic and environmental effects caused by river sediment load. In this context, we evaluate and compare synthetic methods into regression analyses based on classification of discharge and corresponding sediment load data over 31 years in order to increase accuracy and efficiency of sediment rating curve. Various models were tested based on significance level of 0/05 and standard error of estimate (SEE) in SPSS software to select the suitable sediment load estimation model. Results indicated good performance of the exponential model among regression models and the monthly model among temporal models. Thus, the monthly power regression model with the lowest SEE (0/81) was selected as the proper model for estimating suspended sediment load. In contrary, the temporal model used for fitting regression functions to discharge and sediment load data based on all data without classification, was recognized as the most non-efficient model. Finally, it is concluded that the monthly model can be useful to know of sediment yield regime and complexity of sediment load transport in the watershed.

Because of the importance of sediment transport in the efficient use of water resources and dam designing, estimation of the sediment load in rivers has been an essential interest to the engineers from long time ago. This leads to the design of various methods such as different empirical equations for solving the sediment transport. The error in most traditional experimental methods is common due to complexities in how this process works out and the vast amount of factors that cause this phenomenon. Therefore, achieving a suitable method that can accurately estimate the amount of sediment is very essential. In this study, the suspended sediment loads of SofiChay river have been estimated by modern data mining methods including Gaussian process and support vector machines that use the kernel functions that have a high ability to solve nonlinear problems. The results that obtained were compared with experimental methods such as sediment rating curve and seasonal method.
The study area
Sofi Chay catchment is up to 311 Km3 in area and has been located in the south part of East Azerbaijan province and the northern city of Maragheh. Sofi Chay river is located within the geographical coordinates 37⁰ '15 "2 to 37⁰ '45 3'' north latitude and 45⁰ '56 " 31 to 46⁰ '25 "5 east longitude.
Gaussian process regression
Gaussian processes are a fruitful way of defining prior distributions for flexible regression and classification models in which the regression or class probability functions are not limited to simple parametric forms. One attraction of Gaussian processes is the variety of covariance functions one can choose from. These lead to functions with different degrees of smoothness or different sorts of additive structures. When such a function defines the average response in a regression model with Gaussian errors, we can use matrix calculations to deduce that it is possible for data sets with more than a thousand samples. Gaussian processes in statistical modeling are very important because they are normal characteristics. Gaussian processes and related methods have been used in various contexts for many years. Despite this past usage, and despite the fundamental simplicity of the idea, Gaussian process models appear to have been little appreciated by most Bayesians. I speculate that this could be partly due to confusion between the properties one expects of the true function being modeled and those of the best predictor for this unknown function.
Support Vector Regression (SVR)
SVRs are a subset of SVMs that are particular learning systems that use a linear high dimensional hypothesis space called feature space. These systems are trained using a learning algorithm based on optimization theory. This method was introduced by Vapnik in 1995. SVMs have been employed for regression estimation, so called support vector regression (SVR), in which the real value functions are estimated. In this case, the aim of learning process is to find a function f(x) as an approximation of the value y(x) with minimum risk, and only based on the available independent and identically distributed data. Often in complex nonlinear problems, the original input space (predictor variable) is non-linearly related to the predicted variable (lateral spread displacement).
In this study, after required data related to the Sofi ChayRiver were collected, these data were examined by the standard normal homogeneity tests such as the Buishand range, Pettitt and Von Neumann ratio; and after refining the data, the drawing of sediment rating curve was developed. Then, the amount of sediment discharge of the river Sofi Chay was estimated using Gaussian process regression, support vector regression, sediment rating curve and seasonal methods. To achieve optimum results by the used data mining techniques, various scenarios including different types of kernel functions and different intervals of hyper parameters of kernel functions were defined. When Gaussian process regression, along with radial basis function kernel (Gaussian noise (ɛ) equal to 0.01 and gamma (Υ) equal to 0.5), were used to estimate sediment discharge rate of the river Sofi Chay, it was observed that this method by presenting statistical indicators (correlation coefficient (R) equal to 0.977, Nash-Sutcliffe coefficient (NS) equal to 0.794, mean absolute error (MAE) equal to 77.4278 (tons/day) and root mean square error (RMSE) equal to 698.7455 (tons/day)), have the highest accuracy and lowest error among the methods investigated in this study. Also the both investigated data mining methods have far greater efficiency and accuracy in this area.
In this study, the amount of suspended sediment load was estimated using traditional methods such as sediment rating curve and seasonal method in comparison with modern data mining methods based on kernel functions such as Gaussian process regression and support vector regression. The results indicated that seasonal method has better performance in this case rather than sediment rating curve. The comprehensive results show that both modern data mining methods examined in this study outperform rather than traditional methods. Among the Gaussian process regression and support vector regression results, we observed the higher ability of Gaussian process regression method with using radial basis function as a kernel function. Generally, use of Gaussian process regression method suggested in similar cases.

Suspended load is one of the most important factors in taking decision to create aquatic construction and to determine useful life of dams. Lack of long time measurements of erosion, as well as insufficient researches in this field, limit the access to trustable data and therefore we usually use estimating methods. Whereas sediment rating curve and its adjustment are used as basic methods for estimating suspended sediment load, in this research we used these methods for both of regions. To determine most appropriate method for estimating the suspended load in part of west and north of Iran, flow discharge and corresponding sediment discharge of 12 (6 arid and 6 wet gauges station were analyzed. results showed correlation coefficient in mean load equations is more than single-linear equations in both of wet and arid regions. In the other hand results showed Root Mean Square Error of MVUE factor is less than other Correction factors in both of wet and arid regions.

Suspended sediment transport by rivers is important in water quality of cities and villages fed by certain drainage basins. On the other hand, the effective discharge refers to discharge that transports the most sediment over long time and controls the shape of river bed. Therefore, determination of effective discharge for suspended sediment transport and analysis the variation of effective discharge at temporal scale is very important. The aim of this study is evaluating the effective discharge for sediment transport using flow discharge and sediment discharge for a thirty one year period (1983-2013) in Korkorsar watershed, Mazandaran Province. For this purpose at first, sediment rating curve was provided based on monthly, seasonally and total period time scale by using USBR and clusters average limit methods. Flow discharge was clustered into several equal clusters and mean flow discharge was calculated for each cluster. Then, for each mean flow discharge, amount of suspended sediment was determined based on flow discharge and sediment rating curves. Finally, the histogram of flow discharge and total sediment was plotted and effective discharge was determined. Results indicate monthly effective discharge and seasonally effective discharge vary from 0.24-25.42 m3 s-1 and 4.37-25, respectively. The effective discharge in whole studied period was equal to 24.36 with return period of 100-year. In addition, the comparison of effective discharge in different time scale demonstrated that effective discharge amount could have high variability in monthly and seasonal time scale. Determining the effective discharge and its temporal variation in watersheds plays the effective role on decreasing sediment transport via. appropriate rive reach and land management specially at the time of occurrence of effective discharge.

Being available the accurate data on carried sediment has accounted as an important factor for making decision about constructing of river structures and determining of dam life. To accomplish this object، a number methods have been proposed so that sediment rate curving as an hydrological method has been developed for doing it. Ignoring differences among seasons causes to lower the precision of this method. So، present research has been programmed for evaluation of classified discharge to two categories including high water and low water on suspended sediment calculated by sediment rating curve in comparison with Artificial Neural Network (ANN). For acquiring this object، by means of flow duration curve and USBR method، daily suspended sediment and sediment rating curve were resulted. Finally، some statistical criteria including Relative Error (RE)، Model Efficiency (EF)، Root Mean Square Error (RMSE) and Descriptive Coefficient (R2) were applied for comparing the results outcome of sediment rating curve method and ANN method. Results showed that ANN method has as higher capability in comparison with sediment rating curve on basis of Descriptive Coefficient and Model Efficiency 0/903 and 0/89 respectively moreover Root Mean Square Error and Relative Error 0/322 and 6/22 respectively.

The sediment load that is transported by rivers can create numerous problems such as sedimentation in the reservoir and storage capacity reduction، developed load in the rivers، destruction of structures along the river and transfer of pollution. Therefore، an accurate estimate of the sediment load in rivers is absolutely essential for river engineering، reservoir design، sediment transportation، measuresof environmental damage caused by deposition. Due to the fact sediment load is measured at different times in addition to the lack of reliable observational data in the watershed، empirical equations are used to estimate the sediment load. In this study،to determine the most suitable method for estimating the suspended sediment in Roodak Hydrological Station، we have collected and analyzed data from flow and sediment discharge during the years1972-2008. By the correlation between flow and sediment discharge onthis station of six statistical methods، we chose the best method to estimate suspended load. In a comparison of the accuracy of these methods، we used the statistical indices، RME، MAE and standard deviation. The resultsshowed that the multi-linear (two curves) method wasthe mostaccurate method. Therefore it was selected as the most appropriate method for estimating suspended load in Roodak Station.