Saeid Khosrobeigi Bozchelui; Arash Malekian; Alireza Moghaddam Nia; Shahra,m Khalighi
Abstract
Flood is one of the most devastating natural disasters, causing financial and human losses each year. At the same time, many rivers in Iran's watersheds lack complete and accurate statistics and information. On the other hand, estimating the flow of floods is one of the most important factors for the ...
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Flood is one of the most devastating natural disasters, causing financial and human losses each year. At the same time, many rivers in Iran's watersheds lack complete and accurate statistics and information. On the other hand, estimating the flow of floods is one of the most important factors for the design and implementation of water structures. In such cases, one of the appropriate solutions to estimate the maximum flow rate with different return periods is flood analysis. In order to conduct the present study, 55 hydrometric stations with a common statistical period of 20 years were considered to perform the work after the statistical deficiencies were eliminated. Then, based on the distribution of the third type of Pearson logo with the lowest error rate and the highest number of first rank as the most suitable fit function, the amount of discharge in different return periods was estimated. The following information was collected on the types of physiography, land use, climate and geology variables. After collecting information about all independent variables using Gamma test, the most important variables affecting the maximum instantaneous flow, including area, drainage density, maximum 24-hour rainfall and watershed environment, were selected and modeled using methods. Random forest modeling and support vector modeling were performed and their efficiency was determined based on statistical indicators With an efficiency coefficient of 74 to 83%, the error of 3.05 to 32.11 m3 and the coefficient of explanation of 76 to 91 are more accurate than the random forest model.
Elham Kakaei Lafdani; Ali Reza Moghaddam Nia; Azadeh Ahmadi; Heydar Ebrahimi
Abstract
This study aimed to examine the influence of pre-processing input variables by Gamma Test on performance of Support Vector Machine in order to predict the suspended sediment amount of Doiraj River, located in Ilam Province from 1994-2004. The flow discharge and rainfall were considered as the input variables ...
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This study aimed to examine the influence of pre-processing input variables by Gamma Test on performance of Support Vector Machine in order to predict the suspended sediment amount of Doiraj River, located in Ilam Province from 1994-2004. The flow discharge and rainfall were considered as the input variables and sediment discharge as the output model. Also, the duration of the model training period was determined through GT. Thereafter, in order to carry out the influence of pre-processing input variables on performance of model, the suspended sediment was predicted using SVM model while no pre-processing has been done on its input variables and the results were compared to each other. Results show the performance of the GT-SVM model in the test phase with minimum RMSE was equal to 0.96 (ton/day) and the maximum coefficient of R2 was equal to 0.98 between the predicted and actual values, was better than SVM model.
