Maryam Asadi; Ali Fathzadeh
Abstract
Understanding of suspended sediment rate is one of the fundamental problems in water projects which water engineers consistently have involved with it. Wrong estimations in sediment transport cause incorrect design and destruction of hydraulic systems. Due to the difficulty of suspended sediment measurements, ...
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Understanding of suspended sediment rate is one of the fundamental problems in water projects which water engineers consistently have involved with it. Wrong estimations in sediment transport cause incorrect design and destruction of hydraulic systems. Due to the difficulty of suspended sediment measurements, sediment rating curves is considered as the most common method for estimating the suspended sediment load. The main purpose of this research is the capability challenge of this method in comparison to some state of the art models. In this study, we selected some computational intelligence models (i.e. K-nearest neighbor (KNN), artificial neural networks (ANN), Gaussian processes (GP), decision trees of M5, support vector machine (SVM) and evolutionary support vector machine (ESVM)) and compared them with their sediment rating model in 8 basins located in Gilan province. Daily sediment and discharge data considered as the input data for 30-years. Evaluation of the results indicated that the Gaussian process model has the lowest residual sum of squares (RMSE) and the highest correlation coefficient (r) than the other models.
javad zahiri; hadis shahrokhi; Ahmad Jafari
Abstract
Suspended sediment load estimation is one of the most important and complicated debates on sediment transport and river engineering. Therefore, the variety of methods developed to estimate suspended sediment load in rivers. According to the empirical nature of these methods, the results have low accuracy ...
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Suspended sediment load estimation is one of the most important and complicated debates on sediment transport and river engineering. Therefore, the variety of methods developed to estimate suspended sediment load in rivers. According to the empirical nature of these methods, the results have low accuracy and vary so widely from one method to another. Recently, with advances in computer science, various algorithms such as tree based methods have been developed. In this study, sediment rating curve method along with new algorithms such as M5 and Genetic Programing (GP) are used for estimating suspended sediment load in rivers. Flow and sediment discharge data at five hydrometric stations, Behbahan and Cham Nezam on Maroon River, Jow Kanak on Allah River and Moshrageh and Shadegan on Jarahi River are used in present study. The efficiency of tree models, in all stations, was greater than sediment rating curve method, and the RMSE performance of M5 method is 7 to 41 percent superior to sediment rating curve method. The results of this study indicate the close proximity of both M5 and GP efficiency, which according to the simple and conceptual structure of M5, this method is proposed to estimate suspended sediment load in river streams.
ali rezazadeh joudi; Mohammad Taghi Sattari
Abstract
Estimation of suspended sediment load or specifying the damages incured as a result of inattention to such estimation is one of the most important and fundamental challenges in river engineering and sediment transport studies. Given the importance and role of sediment in the design and maintenance of ...
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Estimation of suspended sediment load or specifying the damages incured as a result of inattention to such estimation is one of the most important and fundamental challenges in river engineering and sediment transport studies. Given the importance and role of sediment in the design and maintenance of hydraulic structures such as dams, as well its significance in planning for efficient tilization of downstream river and also conservation of nutrients at the upstream of river, many attempts have been made to estimate suspended sediment load of rivers and numerical methods have been developed in this regard. But due to the high cost of most procedures or lack of adequate precision in most common experimental methods, a new method is needed that can estimate suspended sediment load with the greatest possible precision. In this study, the amount of suspended sediment load of Lighvan River has been estimated through support vector regression and k-Nearest neighbor methods. Results indicated the appropriateness of both data mining techniques applied in this study. Among examined methods in this study, the support vector regression method predicted the amount of suspended sediment load in LighvanChay River with representing evaluation indexes such as (CC=0.959, RMSE=43.547(ton/day)) more accurately than K-nearest neighbor method
golaleh ghaffari; Hasan Ahmadi; Omid Bahmani; Ali Akbar Nazari Samani
Abstract
Although more than 45 years has passed since the first time operating watershed projects were run inside the country and considering the increasing budget that is being allocated to such projects, terrible soil erosion, natural resource degradation and the dazzling and painful consequences of such events ...
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Although more than 45 years has passed since the first time operating watershed projects were run inside the country and considering the increasing budget that is being allocated to such projects, terrible soil erosion, natural resource degradation and the dazzling and painful consequences of such events are indicative of low efficiency and failure of natural resource protection projects. Therefore, since watershed projects involve many agents and management needs to be as effective as possible, assessment of how suuccessfully the project objectives have been realized is essential. The sologhan aquifer’s basin with an area of 20571 hectar is one of the basins in which watershed project was carried out in winter 2000. To assess the effectiveness of the above mentioned project, required statics and data was collected and the accuracy of meteorology and hydrology data was examined and the double mass curve, annual hydrograph comparisons, hydrologic analysis, sediment examination in dry and moist periods, sediment graph, studding the amount of sediment in precipitation periods prior and post projects implementation were considered and used. Double mass curve shows the positive effects of this operation on the amount of runoff and sediment. Hydrologic regime curves show as well that watershed operations had positive impacts upon the basins hydrologic reaction to rain so that the amount of runoff as a result of similar amount of precipitation has decreased. Furthermore, the amount of annual suspended load has decreased from 47892.56 to 22365 tons, so did the amount of Debbi and sediment. Above results prove the positive effects of watershed projects.
mohsen yosefi; Fatemeh Barzegari
Abstract
Suspended sediment estimation is an important factor from different aspects including, farming, soil conservation, dams, aquatic life, as well as various aspects of the research. There are different methods for suspended sediment estimation. This study aims to estimate suspended sediment using feed forward ...
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Suspended sediment estimation is an important factor from different aspects including, farming, soil conservation, dams, aquatic life, as well as various aspects of the research. There are different methods for suspended sediment estimation. This study aims to estimate suspended sediment using feed forward neural network with error back propagation with Levenberg-Marquardt back propagation algorithm and compare the results with best sediment rating curves among commonly used sediment rating curves, including: linear, seasonal, monthly and Mean load within discharge classes. To attain this, the sediment discharge and the corresponding water discharge data for ten hydrometric stations of Lorestan province of Iran were used. In next step different methods of sediment rating curves along with different correction factors, a total of 20 methods were applied to data. Results showed among examined methods; monthly rating curve with MUVE correction factor has been selected as best, based on Nash and Sutcliffe index and accuracy index. Then results of estimating sediment load by using selected sediment rating curve were compared with the results of the neural network. Mean-square error and Nash and Sutcliffe index were applied to select more appropriate method. The results showed the suitability of the feed forward neural network error propagation in compare with sediment rating curves.
B. Motamedvaziri; H. Ahmadi; M. Mahdavi; F. Sharifi; N. Javaheri
Volume 62, Issue 2 , October 2009, , Pages 283-298
Abstract
Estimation of river sediment load is one of the most important issues in design of hydraulic structures, investigating water quality, conserving fish habitat, estimating erosion and determining watershed management effects. There are two methods for estimating sediment load: empirical and hydrological ...
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Estimation of river sediment load is one of the most important issues in design of hydraulic structures, investigating water quality, conserving fish habitat, estimating erosion and determining watershed management effects. There are two methods for estimating sediment load: empirical and hydrological methods. Existence of numerous empirical methods for estimation of river sediment load and a wide range of calibration coefficients shows that a suitable analytical or empirical method does not yet exist to accurately estimate the sediment load. Also, hydrological methods are not able to recognize and separate the specific data measuring conditions and they can not show the temporal variation of sediment loads. In spite of these problems, nowadays, researchers are using Artificial Intelligence methods such as Fuzzy Logic. In this study, the measured suspended sediment load at hydrometric station of Sarcham located on Zanjanroud river is analyzed using USBR and FAO methods (common hydrological methods). Furthermore, suspended sediment load are estimated with a model developed based on Fuzzy Logic rules. In order to estimate suspended load using fuzzy method, one method named Supervised Fuzzy C- mean Clustering Method, is used. Then the results of hydrological and fuzzy methods are compared. The results showed that the temporal variation of sediment loads can be analyzed using a fuzzy method. Also the results obtained using the fuzzy method in comparison with the corresponding values obtained using the usual hydrological methods shows a better correlation with the observed values.
