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Investigation of Sedimentation Pattern of the River and Reservoir of Kamal-Saleh Dam Using Numerical Model GSTARS4.0
Javad Mozaffari,
year 12, Issue 2 (Summer 2022)
Abstract
1- Introduction
      Accumulation of sediments in dam reservoirs is one of the most important problems in the operation and maintenance of reservoir dams and this phenomenon reduces the useful life of dams. Reservoir sediment changes depend on factors such as the amount of sediment, sediment transport rate, sediment type, reservoir performance, reservoir characteristics and river flow. With the entry of sediment and its accumulation in the reservoir of the dam, the effective water storage capacity decreases. This in turn will reduce water storage capacity and loss of reservoir flood capacity. If sediment accumulates near the dam body, it may cause the bottom drains of the intake valves to be buried and can also impede their operation. In addition, sediment can erode the turbines and the lower valves of the reservoirs. The load on the dam body also increases. In this study, the sedimentation situation in the reservoir of Kamal Saleh Dam during the next 50 years will be investigated.
  1. Methodology
      More than 35 km of the river was considered for simulation. By entering the TIN map into Arc VIEW GIS software, information related to preparing the map for entering into Hec-Ras software was added with the help of Hec-Georas extension. This information included drawing lines related to the coasts, drawing lines related to the flood plain, drawing cross sections and drawing the central line. By entering the information into Hec-Ras software, REPORT output was taken in this software. In this output, the information is obtained as a text file. Therefore, they can be used to enter the text file of GSTARS4 software. In the next step, the required information was entered into GSTARS4 software. This information included 171 cross sections. Also information such as manning roughness, river flow and sediment, sediment model and stage-discharge were considered. To run the model and investigate sedimentation, two modes were considered for examining the water level inside the dam. One mode was the maximum water level in the dam (full dam) and the other was semi-full dam. Due to the water level inside the dam, sedimentation can change in these two cases. Finally, the software was run for 50 years of sedimentation. There are 14 models for sediment transport in GSTARS4 software among which the Acker and the Yang models had a suitable grain size diameter range to be used. The results showed that the use of two models will have similar results and the changes related to erosion, sedimentation and sedimentation front near the reservoir will give similar results for the two models. Therefore, the Yang model was used to continue the work.

3-Results
       The results of model showed that for full and semi-full dam conditions, the sedimentation location will be different, so that in full sediment dam condition, it starts approximately from 10480 meters distance from dam and continues up to 7236 meters and then stops. But for the dam in the semi-full state, the sedimentation starts from 5766 meters and continues up to 2547 meters distance from dam. Therefore, sedimentation in the full dam reservoir starts about 4714 meters earlier and also ends earlier at about 4690 meters distance from the downstream. The distance that sedimentation is done in the case of full dam is 3243 meters and its maximum depth is 14.46 meters and for semi-full dam is 3220 meters and its maximum depth is 12.79 meters. Investigation of the cross sections showed that at a distance that sedimentation occurred in the case of a full dam (10.5 to 7 km), not only sedimentation did not occur for the semi-full dam, but also erosion is observed in the river. Of course, this erosion was obtained in a small width of the river (approximately 20 meters in width). On the other hand, if the Acker-White model was used to investigate sedimentation in the reservoir, erosion in this area was almost not observed in the case of a semi-full dam. Therefore, erosion in this area can be neglected for the semi-full dam and the cross section can be considered for the semi-full dam with the same initial bed. Also, due to the small width of erosion, it can be prevented by erosion control methods.
4- Discussion & Conclusions
       It seems that there is not much difference in the amount of sedimentation in terms of sedimentation distance and its height, and only the sedimentation location has been moved, which in the semi-full dam reservoir, sedimentation is closer to the dam. This shows that the level of water in the dam can have a great impact on sedimentation in its reservoir. Of course, it should be noted that the dam is usually not in full state, so to determine the deposition, two states of full and semi-full dams were considered. Therefore, the deposition front can be placed between these two modes according to the operating condition.
 
Modeling the Erosion and Sedimentation Rate of Sojasrood River before and after Construction of Golaber Dam with Machine Learning Algorithms
Sayyad Asghari Saraskanrood, Hassan Mozaffari, Fariba Esfandiari,
year 12, Issue 2 (Summer 2022)
Abstract
1- Introduction
Dams are one of the most important human structures along rivers that are constructed with the aim of generating electricity, flood control, and providing water for agriculture and urban centers. Today, very few large and small rivers remain uncontrolled; what is important geomorphologically is the changes that occur in the performance of erosion processes downstream of the river after dam construction. These changes are not limited to after dam construction, but these morphological changes are the result of changes in the performance of erosion and deposition processes in drainage basins, completely transforming the face of the basin and canals in the area close to the constructed dam. Analyzing the geomorphological changes of rivers due to the creation of human facilities such as dams is one of the most important tasks of geomorphologists. This study is done with different models and methods. Among the common methods of the last decade are the GCD model and machine learning. The GCD method is the result of subtracting two digital elevation models at different times, which are produced by different methods of these dams. To analyze the geomorphological changes caused by the construction of the dams in downstream of the river, in addition to using historical dams, machine learning methods can be used for more accurate modeling by involving a variety of effective maps in detecting changes. The main purpose of this study is to apply the machine learning method using the data obtained from the GCD model to generate regression maps due to the impact of dam operation downstream of the river.
2- Methodology
This study was carried out in the Sojasrood River and downstream of Golaber dam. Software and tools used in this research included the following: Arcgis, envi, sagagis software, R software, GCD software and extension, Google Earth Engine system, AutoCAD software, Excel, topographic maps of 1/50000, Elevation digital model and Garmin GPS. Machine learning methods were used to evaluate the effects of Golaber Dam in the period before and after the construction of the dam. In order to access the data required for this research, digital elevation models of stereo pair images of L1A series and L1B satellite ester were used as time series. First, through the GCD model, the volume changes of erosion and sediment downstream of the dam were calculated. Then, the data obtained from this model were used as a target variable along with nine layers of geomorphometry and precipitation and runoff as predictive data to implement machine learning algorithms in three methods of multiple linear regression, decision tree, and random forest. 70% of the data were used for modeling and 30% of the data were used for evaluation in R programming software.
3- Results
Given that continuous data on erosion and sedimentation rates from the GCD model have been used for the machine learning method, naturally, a regression method (prediction) should be used for the output of the machine learning models. Three steps were taken to achieve the result of machine learning. First, the models were run one by one in R software and evaluated with 30% of the experimental data, and finally, the model maps and their correlation coefficient and RMSE error were calculated. Comparison of the output results of multiple linear regression models and decision tree and random forest showed differences in statistical data and time series maps before and after dam construction. Therefore, the output maps of the models before and after the construction of the dam were also different from each other. The main reason for this is a significant reduction in the runoff, land-use changes, increased vegetation of the bed and riverside, which has led to changes in the independent variable research data in the period after the construction of the dam. Although statistically in multiple linear regression, the p-value was less than 0.05, the output of maps of this model were associated with a large error. And the model did not predict the rate of erosion and sediment well. In the multiple linear regression model, the correlation coefficient of the map before the construction of the dam was higher than the period after the construction of the dam. CART method was used for decision tree modeling. The map produced by this method with a correlation coefficient above 0.6 showed better performance compared to the multiple linear regression model. The best method for modeling erosion and sedimentation rates in both periods was the random forest method. This model with a correlation coefficient above 0.7 provided the most accurate prediction in this study.
4- Discussion & Conclusions
Various methods and models have been proposed to estimate the rate of erosion and sediment in rivers. In recent years, new and more accurate models have been formed, especially in the analysis of the time series of river developments. New methods include the GCD model and machine learning (ML). In this study, in order to observe the changes in erosion and sedimentation rate due to the construction of Golaber Dam in the period before and after its construction, first, the GCD model of volumetric erosion and sedimentation changes was estimated through the model of published errors and multiple time series maps were produced. Then, from the data obtained from this model, along with maps and geomorphometric layers and maximum rainfall and runoff data, in order to more accurately predict the impact of the dam on the riverbed in terms of erosion and sedimentation rates and geomorphological changes of the river, machine learning method was used. The results of modeling showed that dam utilization was strongly effective in erosion and sedimentation of river bed and Random Forest algorithm with a correlation coefficient above 70% and RMSE less than the other two models showed the best prediction for both periods before and after dam construction. The maps produced by the decision tree method also modeled the erosion and sedimentation process in the riverbed in both time series analyses well, but the output of the linear regression model was not accurate enough. For an overview of machine learning algorithms, in addition to evaluating the experimental data of the models themselves, the overall average results of some morphometric indices of the river such as number of meanders, center angle, channel length, and Sinuosity index were also evaluated. This comparison showed the accuracy of modeling decision tree and random forest algorithms applied in the present study.

Investigation of sand mining potential from the Qarechai river bed
Rezvan Shah Heydari, Javad Mozaffari,
year 13, Issue 2 (Summer 2023 2023)
Abstract
In this study, the sedimentation process in QareChai River (located in Markazi province) and the possibility of sand mining and construction activities in the margin and floodplain areas was evaluated. To do this, by simulating the river flow, the rate of erosion and sedimentation was simulated in a length of 29.8 km near the Khondab city. After preparing the required and basic information and data related to bed materials and sediment gradations, the river sediment flux and distributions along the river were investigated using the HEC-RAS model. After calibrating the model, Wilcock sediment transport formula was selected as the most appropriate equation for estimating sediment discharge with an error of 6% in comparison with the values ​​measured at the hydrometric station. Accordingly, the amount of bed changes in the level of each river section, the amount of sediment and the changes in the longitudinal profile of the river over a 10-year period were simulated. The results showed that during 10 years, there was an average of 13 cm of erosion. The average erosion in the intervals of 6 km from upstream to downstream is 18, 21, 14.8 and 3.2 cm, respectively. Therefore, it is impossible to extract river bed materials in this period. In addition, the creation of a hole with a length of 6 km, a wide of 50 m, and depths of 20 and 50 cm from the deepest point of the river at a distance of 6 km downstream showed that these holes will remain constant after 10 years and will not return to their original conditions before mining. Therefore, when the erosion rate reached its minimum value, it will be possible to extract sediment at a distance of 6 km downstream.
 
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