Hydrogeomorphology
Erfan Bahrami; mehdi dastourani
Abstract
Estimation of flood hydrographs in the ungauged watersheds is a challenging issue in flood planning and management. Various models have been developed in this filed and it is necessary to evaluate the performance of models developed in different regions of the world with different climatic, hydrological ...
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Estimation of flood hydrographs in the ungauged watersheds is a challenging issue in flood planning and management. Various models have been developed in this filed and it is necessary to evaluate the performance of models developed in different regions of the world with different climatic, hydrological and physiographic features in order to comment on their performance in different regions. The Gamma synthetic unit hydrograph model is a developed model for estimating flood hydrographs in the ungauged watersheds with limited studies in the world. In this study, the Gamma synthetic unit hydrograph model for estimating flood hydrograph characteristics in Qareh-Sou watershed located in Kermanshah province in Iran has been investigated. Criteria for percentage error in peak discharge, percentage error in volume, mean absolute error, mean bias error, coefficient of determination and Kling-Gupta were estimated to evaluate the accuracy of simulation results. Based on the results, the mean values of the criteria expressed are 6.28, 17.4, 0.89, 0.54, 0.74 and 0.75, respectively, indicating that the Gamma synthetic unit hydrograph model is quite accurate in estimating the characteristics of the flood hydrograph in this study. In addition, the visual comparison of computational and observational hydrographs illustrates the remarkable accuracy of the Gamma synthetic unit hydrograph model in estimating the shape of flood hydrographs in the studied events.
erfan bahrami; Ali Shahidi
Abstract
were prepared as seven raster layers, and after ranking and weighing, the obtained DRASTIC index ranged between 45 and 115. Yet, as far as the model's major problem is applying expert opinions in ranking and weighing the variables, the main purpose of this study is to improve the DRASTIC model by using ...
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were prepared as seven raster layers, and after ranking and weighing, the obtained DRASTIC index ranged between 45 and 115. Yet, as far as the model's major problem is applying expert opinions in ranking and weighing the variables, the main purpose of this study is to improve the DRASTIC model by using the gene expression model, which as an intelligent model has shown a desirable performance. Also, in a mixed form, it can cope with other models to provide acceptable results. Thus, DRASTIC variables of a 20- year statistical period (1999-2009) were defined as the model input, and nitrate concentration was defined as its output. Data in GEP model were divided into two categories: training and experimentation. Moreover, using the statistical parameters (R2, RMSE, MAE and r), the simulation results of the gene expression model were evaluated. The results indicate the model's high ability in estimating nitrate concentration and its high capability in improving DRASTIC model. For validation and improvement of DRASTIC model, statistical parameters, R2 and r, were used, which were specified according to the error of the range model. Also, for each time combining the parameter with the GEP model, a score was gained during different stages and repeated performances of the weight ranking model using weighing rank model of each parameter. Finally, by removing two parameters, S and T, the modified formula of the DRASTIC index which was obtained based on weighing was 5D, 4R, 5A, 5I, and 4C.
