Hydrogeomorphology
ahmad godarzi; hojatolah younesi; babak shahinejad; hassan torabi
Abstract
In rivers, sediment load monitoring is mainly confined to suspended load measurement; as a result, maximizing resources and reducing the damage caused by river flow is critical. The goal of this study was to use Mike3D.2018 software to create a three-dimensional simulation of the Kashkan river flow in ...
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In rivers, sediment load monitoring is mainly confined to suspended load measurement; as a result, maximizing resources and reducing the damage caused by river flow is critical. The goal of this study was to use Mike3D.2018 software to create a three-dimensional simulation of the Kashkan river flow in the spring of 2019. For this objective, HEC-RAS5.0.7 software is introduced and input according to the production of altitude cultivar (coming from mapping) from the bed and floodplain of the analyzed river with a length of 1200 meters and a scale of 1: 1000 for numerical modeling. Flood, suspended sediment, and transition sediment were estimated using data from the Kashkan-Poldakhtar hydrometric station for return periods of 25, 200, 1000, and 1250 years. Floods were highest at 1200 and 1100 cross sections and lowest at 50 and 350 cross sections, according to the model's findings. By comparing the values with the observed values, it was discovered that the simulation at Kashkan-Poldakhtar hydrometric station performed better. Total sediment simulated 207.45 million tons per day and suspended load utilizing Young’s relation with + 11.87 percent error. The amount of transitional suspended sediments in April (5132779/31) was also higher than in January (9890/55), February (41083/73), March (149629/75), and May (15/112617), according to the findings. In addition, compared to the typical silt in the Kashkan River, the amount of sediment in this month is quite large.
mehran mohammadpanah moghadam
Abstract
1-IntroductionAt 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 ...
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1-IntroductionAt 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.2-MethodologyAbshineh 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.3-Results and DiscussionExamination 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.4- ConclusionThe 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 moreaccuracy and efficiency for estimating suspended sediment in a modified form in the Abshineh basin Hamedan.
Mehdi Hayatzadeh; Sahar Amini; Ali Fathzadeh; Maryam Asadi
Maryam Asadi; Ali Fathzadeh; Roohollah Taghizadeh Mehrjerdi
Volume 4, Issue 10 , June 2017, , Pages 121-143
Abstract
The main purpose of this study is an inquiry into the functions of daily, monthly, and annual scales of sediment data in their estimations using machine learning models. For this purpose, suspended sediment load data for three temporal, daily, monthly, and annual, scales at Ohio station, located in the ...
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The main purpose of this study is an inquiry into the functions of daily, monthly, and annual scales of sediment data in their estimations using machine learning models. For this purpose, suspended sediment load data for three temporal, daily, monthly, and annual, scales at Ohio station, located in the USA, between the years of 1992 and 2014 were selected. In order to choose the best model, some machine learning base models such as artificial neural networks, error back propagation as well as radial basis function, k-nearest neighbor, M5 decision tree, Gaussian process, support vector machine (SVR), evolutionary support vector machine (ESVM), and linear regression (LR) models were run and evaluated. The results of this study showed that the k-nearest neighbor with RMSE=5.28, the data Gaussian process model with RMSE=8.7, and the Gaussian process model with a RMSE=7.2 were respectively the best models for the daily, monthly, and annual data. The comparison of the models' assessment also suggested that the predicted annual data were more accurate than the monthly and daily data.
